90 lines
2.8 KiB
PHP
90 lines
2.8 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Classification\Ensemble;
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use Phpml\Classification\Ensemble\Bagging;
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use Phpml\Classification\DecisionTree;
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use Phpml\Classification\NaiveBayes;
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use Phpml\Classification\Classifier;
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class RandomForest extends Bagging
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{
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/**
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* @var float|string
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*/
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protected $featureSubsetRatio = 'log';
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public function __construct($numClassifier = 50)
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{
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parent::__construct($numClassifier);
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$this->setSubsetRatio(1.0);
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}
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/**
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* This method is used to determine how much of the original columns (features)
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* will be used to construct subsets to train base classifiers.<br>
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*
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* Allowed values: 'sqrt', 'log' or any float number between 0.1 and 1.0 <br>
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*
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* If there are many features that diminishes classification performance, then
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* small values should be preferred, otherwise, with low number of features,
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* default value (0.7) will result in satisfactory performance.
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*
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* @param mixed $ratio string or float should be given
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* @return $this
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* @throws Exception
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*/
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public function setFeatureSubsetRatio($ratio)
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{
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if (is_float($ratio) && ($ratio < 0.1 || $ratio > 1.0)) {
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throw new \Exception("When a float given, feature subset ratio should be between 0.1 and 1.0");
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}
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if (is_string($ratio) && $ratio != 'sqrt' && $ratio != 'log') {
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throw new \Exception("When a string given, feature subset ratio can only be 'sqrt' or 'log' ");
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}
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$this->featureSubsetRatio = $ratio;
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return $this;
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}
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/**
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* RandomForest algorithm is usable *only* with DecisionTree
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*
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* @param string $classifier
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* @param array $classifierOptions
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* @return $this
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*/
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public function setClassifer(string $classifier, array $classifierOptions = [])
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{
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if ($classifier != DecisionTree::class) {
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throw new \Exception("RandomForest can only use DecisionTree as base classifier");
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}
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return parent::setClassifer($classifier, $classifierOptions);
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}
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/**
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* @param DecisionTree $classifier
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* @param int $index
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* @return DecisionTree
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*/
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protected function initSingleClassifier($classifier, $index)
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{
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if (is_float($this->featureSubsetRatio)) {
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$featureCount = (int)($this->featureSubsetRatio * $this->featureCount);
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} elseif ($this->featureCount == 'sqrt') {
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$featureCount = (int)sqrt($this->featureCount) + 1;
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} else {
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$featureCount = (int)log($this->featureCount, 2) + 1;
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}
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if ($featureCount >= $this->featureCount) {
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$featureCount = $this->featureCount;
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}
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return $classifier->setNumFeatures($featureCount);
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}
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}
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