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<?php
declare(strict_types=1);
namespace Phpml\Helper;
use Phpml\Classification\Classifier;
trait OneVsRest
{
/**
* @var array
*/
protected $classifiers = [];
/**
* All provided training targets' labels.
*
* @var array
*/
protected $allLabels = [];
/**
* @var array
*/
protected $costValues = [];
/**
* Train a binary classifier in the OvR style
*/
public function train(array $samples, array $targets): void
{
// Clears previous stuff.
$this->reset();
$this->trainByLabel($samples, $targets);
}
/**
* Resets the classifier and the vars internally used by OneVsRest to create multiple classifiers.
*/
public function reset(): void
{
$this->classifiers = [];
$this->allLabels = [];
$this->costValues = [];
$this->resetBinary();
}
protected function trainByLabel(array $samples, array $targets, array $allLabels = []): void
{
// Overwrites the current value if it exist. $allLabels must be provided for each partialTrain run.
$this->allLabels = count($allLabels) === 0 ? array_keys(array_count_values($targets)) : $allLabels;
sort($this->allLabels, SORT_STRING);
// If there are only two targets, then there is no need to perform OvR
if (count($this->allLabels) === 2) {
// Init classifier if required.
if (count($this->classifiers) === 0) {
$this->classifiers[0] = $this->getClassifierCopy();
}
$this->classifiers[0]->trainBinary($samples, $targets, $this->allLabels);
} else {
// Train a separate classifier for each label and memorize them
foreach ($this->allLabels as $label) {
// Init classifier if required.
if (!isset($this->classifiers[$label])) {
$this->classifiers[$label] = $this->getClassifierCopy();
}
[$binarizedTargets, $classifierLabels] = $this->binarizeTargets($targets, $label);
$this->classifiers[$label]->trainBinary($samples, $binarizedTargets, $classifierLabels);
}
}
// If the underlying classifier is capable of giving the cost values
// during the training, then assign it to the relevant variable
// Adding just the first classifier cost values to avoid complex average calculations.
$classifierref = reset($this->classifiers);
if (method_exists($classifierref, 'getCostValues')) {
$this->costValues = $classifierref->getCostValues();
}
}
/**
* Returns an instance of the current class after cleaning up OneVsRest stuff.
*/
protected function getClassifierCopy(): Classifier
{
// Clone the current classifier, so that
// we don't mess up its variables while training
// multiple instances of this classifier
$classifier = clone $this;
$classifier->reset();
return $classifier;
}
/**
* @return mixed
*/
protected function predictSample(array $sample)
{
if (count($this->allLabels) === 2) {
return $this->classifiers[0]->predictSampleBinary($sample);
}
$probs = [];
foreach ($this->classifiers as $label => $predictor) {
$probs[$label] = $predictor->predictProbability($sample, $label);
}
arsort($probs, SORT_NUMERIC);
return key($probs);
}
/**
* Each classifier should implement this method instead of train(samples, targets)
*/
abstract protected function trainBinary(array $samples, array $targets, array $labels);
/**
* To be overwritten by OneVsRest classifiers.
*/
abstract protected function resetBinary(): void;
/**
* Each classifier that make use of OvR approach should be able to
* return a probability for a sample to belong to the given label.
*
* @return mixed
*/
abstract protected function predictProbability(array $sample, string $label);
/**
* Each classifier should implement this method instead of predictSample()
*
* @return mixed
*/
abstract protected function predictSampleBinary(array $sample);
/**
* Groups all targets into two groups: Targets equal to
* the given label and the others
*
* $targets is not passed by reference nor contains objects so this method
* changes will not affect the caller $targets array.
*
* @param mixed $label
*
* @return array Binarized targets and target's labels
*/
private function binarizeTargets(array $targets, $label): array
{
$notLabel = "not_${label}";
foreach ($targets as $key => $target) {
$targets[$key] = $target == $label ? $label : $notLabel;
}
$labels = [$label, $notLabel];
return [$targets, $labels];
}
}