138 lines
3.5 KiB
PHP
138 lines
3.5 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Helper;
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trait OneVsRest
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{
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/**
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* @var array
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*/
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protected $samples = [];
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/**
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* @var array
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*/
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protected $targets = [];
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/**
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* @var array
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*/
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protected $classifiers;
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/**
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* @var array
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*/
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protected $labels;
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/**
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* @var array
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*/
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protected $costValues;
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/**
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* Train a binary classifier in the OvR style
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*
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* @param array $samples
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* @param array $targets
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*/
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public function train(array $samples, array $targets)
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{
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// Clone the current classifier, so that
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// we don't mess up its variables while training
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// multiple instances of this classifier
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$classifier = clone $this;
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$this->classifiers = [];
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// If there are only two targets, then there is no need to perform OvR
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$this->labels = array_keys(array_count_values($targets));
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if (count($this->labels) == 2) {
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$classifier->trainBinary($samples, $targets);
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$this->classifiers[] = $classifier;
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} else {
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// Train a separate classifier for each label and memorize them
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$this->samples = $samples;
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$this->targets = $targets;
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foreach ($this->labels as $label) {
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$predictor = clone $classifier;
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$targets = $this->binarizeTargets($label);
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$predictor->trainBinary($samples, $targets);
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$this->classifiers[$label] = $predictor;
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}
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}
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// If the underlying classifier is capable of giving the cost values
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// during the training, then assign it to the relevant variable
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if (method_exists($this->classifiers[0], 'getCostValues')) {
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$this->costValues = $this->classifiers[0]->getCostValues();
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}
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}
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/**
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* Groups all targets into two groups: Targets equal to
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* the given label and the others
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*
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* @param mixed $label
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*/
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private function binarizeTargets($label)
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{
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$targets = [];
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foreach ($this->targets as $target) {
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$targets[] = $target == $label ? $label : "not_$label";
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}
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return $targets;
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}
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/**
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* @param array $sample
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*
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* @return mixed
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*/
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protected function predictSample(array $sample)
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{
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if (count($this->labels) == 2) {
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return $this->classifiers[0]->predictSampleBinary($sample);
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}
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$probs = [];
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foreach ($this->classifiers as $label => $predictor) {
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$probs[$label] = $predictor->predictProbability($sample, $label);
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}
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arsort($probs, SORT_NUMERIC);
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return key($probs);
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}
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/**
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* Each classifier should implement this method instead of train(samples, targets)
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*
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* @param array $samples
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* @param array $targets
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*/
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abstract protected function trainBinary(array $samples, array $targets);
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/**
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* Each classifier that make use of OvR approach should be able to
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* return a probability for a sample to belong to the given label.
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*
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* @param array $sample
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*
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* @return mixed
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*/
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abstract protected function predictProbability(array $sample, string $label);
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/**
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* Each classifier should implement this method instead of predictSample()
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*
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* @param array $sample
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*
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* @return mixed
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*/
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abstract protected function predictSampleBinary(array $sample);
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}
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