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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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* 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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} |
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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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php-ai /
php-ml
