camspiers/statistical-classifier

A PHP implementation of Complement Naive Bayes and SVM statistical classifiers, including a structure for building other classifier, multiple data sources and multiple caching backends

0.8.0 2014-01-05 22:29 UTC

Requires

• php: >=5.3.3
• symfony/config: ~2.2
• symfony/options-resolver: ~2.2

Requires (Dev)

• maximebf/cachecache: ~1.0
• mikey179/vfsstream: ~1.2
• phpunit/phpunit: ~3.7

Suggests

• camspiers/porter-stemmer: Using a stemmer can help with language based classification
• maximebf/cachecache: Using caching will help improve performance on large datasets

MIT

• Cam Spiers

• bayes
• classifier
• naive
• svm

README

PHP Classifier uses semantic versioning, it is currently at major version 0, so the public API should not be considered stable.

What is it?

PHP Classifier is a text classification library with a focus on reuse, customizability and performance. Classifiers can be used for many purposes, but are particularly useful in detecting spam.

Features

• Complement Naive Bayes Classifier
• SVM (libsvm) Classifier
• Highly customizable (easily modify or build your own classifier)
• Command-line interface via separate library (phar archive)
• Multiple data import types to get your data into the classifier (Directory of files, Database queries, Json, Serialized arrays)
• Multiple types of model caching
• Compatible with HipHop VM

Installation

$ composer require camspiers/statistical-classifier

SVM Support

For SVM Support both libsvm and php-svm are required. For installation intructions refer to php-svm.

Usage

Non-cached Naive Bayes

use Camspiers\StatisticalClassifier\Classifier\ComplementNaiveBayes;
use Camspiers\StatisticalClassifier\DataSource\DataArray;

$source = new DataArray();
$source->addDocument('spam', 'Some spam document');
$source->addDocument('spam', 'Another spam document');
$source->addDocument('ham', 'Some ham document');
$source->addDocument('ham', 'Another ham document');

$classifier = new ComplementNaiveBayes($source);
$classifier->is('ham', 'Some ham document'); // bool(true)
$classifier->classify('Some ham document'); // string "ham"

Non-cached SVM

use Camspiers\StatisticalClassifier\Classifier\SVM;
use Camspiers\StatisticalClassifier\DataSource\DataArray;

$source = new DataArray()
$source->addDocument('spam', 'Some spam document');
$source->addDocument('spam', 'Another spam document');
$source->addDocument('ham', 'Some ham document');
$source->addDocument('ham', 'Another ham document');

$classifier = new SVM($source);
$classifier->is('ham', 'Some ham document'); // bool(true)
$classifier->classify('Some ham document'); // string "ham"

Caching models

Caching models requires maximebf/CacheCache which can be installed via packagist. Additional caching systems can be easily integrated.

Cached Naive Bayes

use Camspiers\StatisticalClassifier\Classifier\ComplementNaiveBayes;
use Camspiers\StatisticalClassifier\Model\CachedModel;
use Camspiers\StatisticalClassifier\DataSource\DataArray;

$source = new DataArray();
$source->addDocument('spam', 'Some spam document');
$source->addDocument('spam', 'Another spam document');
$source->addDocument('ham', 'Some ham document');
$source->addDocument('ham', 'Another ham document');

$model = new CachedModel(
	'mycachename',
	new CacheCache\Cache(
		new CacheCache\Backends\File(
			array(
				'dir' => __DIR__
			)
		)
	)
);

$classifier = new ComplementNaiveBayes($source, $model);
$classifier->is('ham', 'Some ham document'); // bool(true)
$classifier->classify('Some ham document'); // string "ham"

Cached SVM

use Camspiers\StatisticalClassifier\Classifier\SVM;
use Camspiers\StatisticalClassifier\Model\SVMCachedModel;
use Camspiers\StatisticalClassifier\DataSource\DataArray;

$source = new DataArray();
$source->addDocument('spam', 'Some spam document');
$source->addDocument('spam', 'Another spam document');
$source->addDocument('ham', 'Some ham document');
$source->addDocument('ham', 'Another ham document');

$model = new Model\SVMCachedModel(
	__DIR__ . '/model.svm',
	new CacheCache\Cache(
		new CacheCache\Backends\File(
			array(
				'dir' => __DIR__
			)
		)
	)
);

$classifier = new SVM($source, $model);
$classifier->is('ham', 'Some ham document'); // bool(true)
$classifier->classify('Some ham document'); // string "ham"

Unit testing

statistical-classifier/ $ composer install --dev
statistical-classifier/ $ phpunit