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<?php |
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declare(strict_types=1); |
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namespace Phpml\Classification\Linear; |
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use Phpml\Helper\Predictable; |
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use Phpml\Helper\Trainable; |
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use Phpml\Classification\Classifier; |
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use Phpml\Classification\DecisionTree; |
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use Phpml\Classification\DecisionTree\DecisionTreeLeaf; |
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class DecisionStump extends DecisionTree |
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{ |
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use Trainable, Predictable; |
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/** |
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* @var int |
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*/ |
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protected $columnIndex; |
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/** |
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* Sample weights : If used the optimization on the decision value |
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* will take these weights into account. If not given, all samples |
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* will be weighed with the same value of 1 |
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* |
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* @var array |
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*/ |
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protected $weights = null; |
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/** |
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* Lowest error rate obtained while training/optimizing the model |
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* |
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* @var float |
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*/ |
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protected $trainingErrorRate; |
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/** |
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* A DecisionStump classifier is a one-level deep DecisionTree. It is generally |
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* used with ensemble algorithms as in the weak classifier role. <br> |
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* |
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* If columnIndex is given, then the stump tries to produce a decision node |
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* on this column, otherwise in cases given the value of -1, the stump itself |
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* decides which column to take for the decision (Default DecisionTree behaviour) |
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* |
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* @param int $columnIndex |
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*/ |
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public function __construct(int $columnIndex = -1) |
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{ |
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$this->columnIndex = $columnIndex; |
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parent::__construct(1); |
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} |
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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 $targets) |
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{ |
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if ($this->columnIndex > count($samples[0]) - 1) { |
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$this->columnIndex = -1; |
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} |
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if ($this->columnIndex >= 0) { |
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$this->setSelectedFeatures([$this->columnIndex]); |
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} |
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if ($this->weights) { |
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$numWeights = count($this->weights); |
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if ($numWeights != count($samples)) { |
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throw new \Exception("Number of sample weights does not match with number of samples"); |
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} |
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} else { |
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$this->weights = array_fill(0, count($samples), 1); |
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} |
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parent::train($samples, $targets); |
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$this->columnIndex = $this->tree->columnIndex; |
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// For numerical values, try to optimize the value by finding a different threshold value |
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if ($this->columnTypes[$this->columnIndex] == self::CONTINUOS) { |
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$this->optimizeDecision($samples, $targets); |
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} |
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} |
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/** |
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* Used to set sample weights. |
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* |
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* @param array $weights |
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*/ |
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public function setSampleWeights(array $weights) |
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{ |
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$this->weights = $weights; |
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} |
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/** |
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* Returns the training error rate, the proportion of wrong predictions |
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* over the total number of samples |
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* |
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* @return float |
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*/ |
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public function getTrainingErrorRate() |
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{ |
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return $this->trainingErrorRate; |
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} |
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/** |
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* Tries to optimize the threshold by probing a range of different values |
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* between the minimum and maximum values in the selected column |
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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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protected function optimizeDecision(array $samples, array $targets) |
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{ |
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$values = array_column($samples, $this->columnIndex); |
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$minValue = min($values); |
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$maxValue = max($values); |
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$stepSize = ($maxValue - $minValue) / 100.0; |
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$leftLabel = $this->tree->leftLeaf->classValue; |
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$rightLabel= $this->tree->rightLeaf->classValue; |
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$bestOperator = $this->tree->operator; |
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$bestThreshold = $this->tree->numericValue; |
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$bestErrorRate = $this->calculateErrorRate( |
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$bestThreshold, $bestOperator, $values, $targets, $leftLabel, $rightLabel); |
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foreach (['<=', '>'] as $operator) { |
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for ($step = $minValue; $step <= $maxValue; $step+= $stepSize) { |
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$threshold = (float)$step; |
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$errorRate = $this->calculateErrorRate( |
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$threshold, $operator, $values, $targets, $leftLabel, $rightLabel); |
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if ($errorRate < $bestErrorRate) { |
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$bestErrorRate = $errorRate; |
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$bestThreshold = $threshold; |
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$bestOperator = $operator; |
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} |
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}// for |
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} |
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// Update the tree node value |
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$this->tree->numericValue = $bestThreshold; |
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$this->tree->operator = $bestOperator; |
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$this->tree->value = "$bestOperator $bestThreshold"; |
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$this->trainingErrorRate = $bestErrorRate; |
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} |
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/** |
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* Calculates the ratio of wrong predictions based on the new threshold |
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* value given as the parameter |
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* |
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* @param float $threshold |
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* @param string $operator |
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* @param array $values |
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* @param array $targets |
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* @param mixed $leftLabel |
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* @param mixed $rightLabel |
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*/ |
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protected function calculateErrorRate(float $threshold, string $operator, array $values, array $targets, $leftLabel, $rightLabel) |
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{ |
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$total = (float) array_sum($this->weights); |
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$wrong = 0; |
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foreach ($values as $index => $value) { |
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eval("\$predicted = \$value $operator \$threshold ? \$leftLabel : \$rightLabel;"); |
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if ($predicted != $targets[$index]) { |
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$wrong += $this->weights[$index]; |
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} |
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} |
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return $wrong / $total; |
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} |
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} |
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php-ai /
php-ml
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This check marks implicit conversions of arrays to boolean values in a comparison. While in PHP an empty array is considered to be equal (but not identical) to false, this is not always apparent.
Consider making the comparison explicit by using
empty(..)or! empty(...)instead.