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AdaBoost algorithm along with some improvements (#51)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,190 @@ | ||
| <?php | ||
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| declare(strict_types=1); | ||
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| namespace Phpml\Classification\Ensemble; | ||
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| use Phpml\Classification\Linear\DecisionStump; | ||
| use Phpml\Classification\Classifier; | ||
| use Phpml\Helper\Predictable; | ||
| use Phpml\Helper\Trainable; | ||
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| class AdaBoost implements Classifier | ||
| { | ||
| use Predictable, Trainable; | ||
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| /** | ||
| * Actual labels given in the targets array | ||
| * @var array | ||
| */ | ||
| protected $labels = []; | ||
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| /** | ||
| * @var int | ||
| */ | ||
| protected $sampleCount; | ||
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| /** | ||
| * @var int | ||
| */ | ||
| protected $featureCount; | ||
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| /** | ||
| * Number of maximum iterations to be done | ||
| * | ||
| * @var int | ||
| */ | ||
| protected $maxIterations; | ||
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| /** | ||
| * Sample weights | ||
| * | ||
| * @var array | ||
| */ | ||
| protected $weights = []; | ||
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| /** | ||
| * Base classifiers | ||
| * | ||
| * @var array | ||
| */ | ||
| protected $classifiers = []; | ||
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| /** | ||
| * Base classifier weights | ||
| * | ||
| * @var array | ||
| */ | ||
| protected $alpha = []; | ||
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| /** | ||
| * ADAptive BOOSTing (AdaBoost) is an ensemble algorithm to | ||
| * improve classification performance of 'weak' classifiers such as | ||
| * DecisionStump (default base classifier of AdaBoost). | ||
| * | ||
| */ | ||
| public function __construct(int $maxIterations = 30) | ||
| { | ||
| $this->maxIterations = $maxIterations; | ||
| } | ||
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| /** | ||
| * @param array $samples | ||
| * @param array $targets | ||
| */ | ||
| public function train(array $samples, array $targets) | ||
| { | ||
| // Initialize usual variables | ||
| $this->labels = array_keys(array_count_values($targets)); | ||
| if (count($this->labels) != 2) { | ||
| throw new \Exception("AdaBoost is a binary classifier and can only classify between two classes"); | ||
| } | ||
|
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| // Set all target values to either -1 or 1 | ||
| $this->labels = [1 => $this->labels[0], -1 => $this->labels[1]]; | ||
| foreach ($targets as $target) { | ||
| $this->targets[] = $target == $this->labels[1] ? 1 : -1; | ||
| } | ||
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| $this->samples = array_merge($this->samples, $samples); | ||
| $this->featureCount = count($samples[0]); | ||
| $this->sampleCount = count($this->samples); | ||
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| // Initialize AdaBoost parameters | ||
| $this->weights = array_fill(0, $this->sampleCount, 1.0 / $this->sampleCount); | ||
| $this->classifiers = []; | ||
| $this->alpha = []; | ||
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| // Execute the algorithm for a maximum number of iterations | ||
| $currIter = 0; | ||
| while ($this->maxIterations > $currIter++) { | ||
| // Determine the best 'weak' classifier based on current weights | ||
| // and update alpha & weight values at each iteration | ||
| list($classifier, $errorRate) = $this->getBestClassifier(); | ||
| $alpha = $this->calculateAlpha($errorRate); | ||
| $this->updateWeights($classifier, $alpha); | ||
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| $this->classifiers[] = $classifier; | ||
| $this->alpha[] = $alpha; | ||
| } | ||
| } | ||
|
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| /** | ||
| * Returns the classifier with the lowest error rate with the | ||
| * consideration of current sample weights | ||
| * | ||
| * @return Classifier | ||
| */ | ||
| protected function getBestClassifier() | ||
| { | ||
| // This method works only for "DecisionStump" classifier, for now. | ||
| // As a future task, it will be generalized enough to work with other | ||
| // classifiers as well | ||
| $minErrorRate = 1.0; | ||
| $bestClassifier = null; | ||
| for ($i=0; $i < $this->featureCount; $i++) { | ||
| $stump = new DecisionStump($i); | ||
| $stump->setSampleWeights($this->weights); | ||
| $stump->train($this->samples, $this->targets); | ||
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| $errorRate = $stump->getTrainingErrorRate(); | ||
| if ($errorRate < $minErrorRate) { | ||
| $bestClassifier = $stump; | ||
| $minErrorRate = $errorRate; | ||
| } | ||
| } | ||
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| return [$bestClassifier, $minErrorRate]; | ||
| } | ||
|
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| /** | ||
| * Calculates alpha of a classifier | ||
| * | ||
| * @param float $errorRate | ||
| * @return float | ||
| */ | ||
| protected function calculateAlpha(float $errorRate) | ||
| { | ||
| if ($errorRate == 0) { | ||
| $errorRate = 1e-10; | ||
| } | ||
| return 0.5 * log((1 - $errorRate) / $errorRate); | ||
| } | ||
|
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| /** | ||
| * Updates the sample weights | ||
| * | ||
| * @param DecisionStump $classifier | ||
| * @param float $alpha | ||
| */ | ||
| protected function updateWeights(DecisionStump $classifier, float $alpha) | ||
| { | ||
| $sumOfWeights = array_sum($this->weights); | ||
| $weightsT1 = []; | ||
| foreach ($this->weights as $index => $weight) { | ||
| $desired = $this->targets[$index]; | ||
| $output = $classifier->predict($this->samples[$index]); | ||
|
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| $weight *= exp(-$alpha * $desired * $output) / $sumOfWeights; | ||
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| $weightsT1[] = $weight; | ||
| } | ||
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| $this->weights = $weightsT1; | ||
| } | ||
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| /** | ||
| * @param array $sample | ||
| * @return mixed | ||
| */ | ||
| public function predictSample(array $sample) | ||
| { | ||
| $sum = 0; | ||
| foreach ($this->alpha as $index => $alpha) { | ||
| $h = $this->classifiers[$index]->predict($sample); | ||
| $sum += $h * $alpha; | ||
| } | ||
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| return $this->labels[ $sum > 0 ? 1 : -1]; | ||
| } | ||
| } |
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