<?php
/**
 * This source file is part of the open source project
 * ExpressionEngine (https://expressionengine.com)
 *
 * @link      https://expressionengine.com/
 * @copyright Copyright (c) 2003-2022, Packet Tide, LLC (https://www.packettide.com)
 * @license   https://expressionengine.com/license Licensed under Apache License, Version 2.0
 */

namespace ExpressionEngine\Addons\Spam\Library;

/**
 * Spam Classifier
 */
class Classifier
{
    public $classes = array();

    // Sensitivity of the classifier, are we at least X% sure this is spam?
    public $sensitivity = .5;
    public $corpus;
    public $training;

    // This is the assumed a priori spam to ham ratio
    public $ratio = .8;

    /**
     * Train the classifier on the provided training corpus
     *
     * @param array $training  An array of feature vectors using classes as keys
     * @param Collection  $vocabulary An instantiatied Collection
     * @access public
     * @return void
     */
    public function __construct($training, $vocabulary, $stop_words = array())
    {
        $this->classes = array_unique(array_keys($training));
        $this->corpus = $vocabulary;
        $this->training = $training;
        $this->sensitivity = (ee()->config->item('spam_sensitivity') / 100) ?: $this->sensitivity;
    }

    /**
     * Returns the probability that a given text belongs to the specified class.
     * This uses a gaussian naive bayes classifier.
     *
     * @param string $source  The text to be classified.
     * @param string $class   The class to test for.
     * @access public
     * @return void
     */
    public function classify($source, $class)
    {
        $orig = $source;
        $source = $this->corpus->transform($source);
        $other = array_diff($this->classes, array($class));
        $other = array_shift($other);
        $class = $this->training[$class];
        $other = $this->training[$other];
        $probabilities = array();
        $log_sum = 0;

        // We want to calculate Pr(Spam|F) ∀ F ∈ Features
        // We assume statistical independence for all features and multiply together
        // to calculcate the probability the source is spam
        foreach ($source as $feature => $freq) {
            if (empty($class[$feature]) || empty($other[$feature])) {
                // We might have more features then currently trained
                // so we'll ignore them until retrained
                continue;
            }

            $class_dist = $class[$feature];
            $other_dist = $other[$feature];
            $class_prob = $class_dist->probability($freq);
            $other_prob = $other_dist->probability($freq);

            // If we don't have enough info to compute a prior simply default to the spam ratio
            $epsilon = 0.01;

            if ($class_prob < $epsilon || $other_prob < $epsilon) {
                $prob = 1 - $this->ratio;
            } else {
                $prob = $class_prob * $this->ratio;
                $prob = $prob / ($prob + $other_prob * (1 - $this->ratio));
            }

            // Must calculate the product in the log domain to avoid underflow
            // so our product becomes a sum of logs
            $log_sum += log($prob) - log(1 - $prob);
        }

        $probability = 1 / (1 + pow(M_E, $log_sum));

        return $probability > $this->sensitivity;
    }
}

// EOF
