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