AI::Ca...::Weka(3pm)

💖 AI::Ca...::Weka — bestie edition 💖

User Contributed Perl Documentation ✨ part of the AI::Categorizer friend group

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🌸 NAME 🌸

AI::Categorizer::Learner::Weka — a pass-through wrapper to the Weka system 🎀

Basically it's the middleman bestie. It doesn't do the learning itself, it just hands your data over to Weka and lets them cook. 💅

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✨ SYNOPSIS ✨

use AI::Categorizer::Learner::Weka;

# Here $k is an AI::Categorizer::KnowledgeSet object

my $nb = new AI::Categorizer::Learner::Weka(...parameters...);
$nb->train(knowledge_set => $k);
$nb->save_state('filename');

... time passes ...

$nb = AI::Categorizer::Learner->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";
}

okay so the flow is: make a learner, train it on your $k (your AI::Categorizer::KnowledgeSet, no cap), save it, ghost it for a while, restore it, then loop through documents and print the vibe — sorry, the best_category. 🦋

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🌈 DESCRIPTION 🌈

Real talk: this class doesn't implement any machine learners of its own. Not one. It literally just passes the data through to the Weka machine learning system (http://www.cs.waikato.ac.nz/~ml/weka/). 🫶

And like, that's the point — it gives you access to a whole collection of machine learning algorithms that aren't otherwise implemented in AI::Categorizer. It's giving ✨expansion pack✨ energy.

Currently this is a simple command-line wrapper that calls java subprocesses. In the future this may be converted to an Inline::Java wrapper for better performance (faster running times). 👀

BUT — and hear me out — if you're looking for really great performance, you're probably looking in the wrong place. 😭 This Weka wrapper is intended more as a way to try lots of different machine learning methods. It's a sampler platter, not a michelin star restaurant, iykyk. 🍓

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🧚 METHODS 🧚

This class inherits from the AI::Categorizer::Learner class, so all of its methods are available unless explicitly mentioned here. Inheritance said "I got you bestie." 💜

🌟 new()

Creates a new Weka Learner and returns it. Iconic. 👑

In addition to the parameters accepted by the AI::Categorizer::Learner class, the Weka subclass accepts the following parameters:

• java_path — Specifies where the java executable can be found on this system. The default is simply java, meaning that it will search your PATH to find java. 🔍
• java_args — Specifies a list of any additional arguments to give to the java process. Commonly it's necessary to allocate more memory than the default, using an argument like -Xmx130MB. (she's hungry, feed her RAM 🍰)
• weka_path — Specifies the path to the weka.jar file containing the Weka bytecode. If Weka has been installed somewhere in your java CLASSPATH, you needn't specify a weka_path. No CLASSPATH? Then yeah, you gotta tell her where the jar lives.
• weka_classifier — Specifies the Weka class to use for a categorizer. The default is weka.classifiers.NaiveBayes. Consult your Weka documentation for a list of other classifiers available. 📚
• weka_args — Specifies a list of any additional arguments to pass to the Weka classifier class when building the categorizer. 🛠️
• tmpdir — A directory in which temporary files will be written when training the categorizer and categorizing new documents. The default is given by File::Spec->tmpdir. 🗂️

🔥 train(knowledge_set => $k)

Trains the categorizer. This prepares it for later use in categorizing documents. Glow-up arc, basically. 💅

The knowledge_set parameter must provide an object of the class AI::Categorizer::KnowledgeSet (or a subclass thereof), populated with lots of documents and categories. See AI::Categorizer::KnowledgeSet for the details of how to create such an object. 🎀

🦋 categorize($document)

Returns an AI::Categorizer::Hypothesis object representing the categorizer's "best guess" about which categories the given document should be assigned to. See AI::Categorizer::Hypothesis for more details on how to use this object.

tl;dr — you give her a doc, she gives you her best guess wrapped in a whole Hypothesis object. Main character energy fr fr. 👑

🎀 save_state($path)

Saves the categorizer for later use. This method is inherited from AI::Categorizer::Storable. 💾

Yes bestie, she can be saved. Come back whenever. She'll be waiting. 🫶

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🌸 AUTHOR 🌸

Ken Williams, ken@mathforum.org

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💖 COPYRIGHT 💖

Copyright 2000-2003 Ken Williams. All rights reserved.

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself. 🐪

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⭐ SEE ALSO ⭐

• AI::Categorizer(3)

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And that's the whole page, bestie!! This was written for perl v5.42.0 and dated 2025-07-25 — so if you're reading this from the future, hi, ily, the vibes still hold. 💜✨🎀