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

version 0.0.4

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,
);

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.

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.

$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*.

$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.

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

The opposite of train().

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

Gets the most probable category the provided item in is.

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

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

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.

• Add support for additive smoothing

Redis, Redis::Bayes, Algorithm::NaiveBayes

• Caio Romão [email protected]
• Stanislaw Pusep [email protected]

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

This is free software, licensed under:

The MIT (X11) License