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<?php |
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declare(strict_types=1); |
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namespace Phpml\Classification\Ensemble; |
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use Phpml\Classification\Ensemble\Bagging; |
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use Phpml\Classification\DecisionTree; |
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use Phpml\Classification\NaiveBayes; |
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use Phpml\Classification\Classifier; |
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class RandomForest extends Bagging |
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{ |
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/** |
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* @var float|string |
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*/ |
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protected $featureSubsetRatio = 'log'; |
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/** |
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* @var array |
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*/ |
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protected $columnNames = null; |
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/** |
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* Initializes RandomForest with the given number of trees. More trees |
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* may increase the prediction performance while it will also substantially |
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* increase the processing time and the required memory |
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* |
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* @param type $numClassifier |
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*/ |
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public function __construct($numClassifier = 50) |
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{ |
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parent::__construct($numClassifier); |
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$this->setSubsetRatio(1.0); |
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} |
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/** |
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* This method is used to determine how many of the original columns (features) |
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* will be used to construct subsets to train base classifiers.<br> |
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* |
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* Allowed values: 'sqrt', 'log' or any float number between 0.1 and 1.0 <br> |
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* |
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* Default value for the ratio is 'log' which results in log(numFeatures, 2) + 1 |
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* features to be taken into consideration while selecting subspace of features |
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* |
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* @param mixed $ratio string or float should be given |
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* @return $this |
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* @throws Exception |
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*/ |
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public function setFeatureSubsetRatio($ratio) |
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{ |
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if (is_float($ratio) && ($ratio < 0.1 || $ratio > 1.0)) { |
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throw new \Exception("When a float given, feature subset ratio should be between 0.1 and 1.0"); |
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} |
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if (is_string($ratio) && $ratio != 'sqrt' && $ratio != 'log') { |
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throw new \Exception("When a string given, feature subset ratio can only be 'sqrt' or 'log' "); |
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} |
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$this->featureSubsetRatio = $ratio; |
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return $this; |
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} |
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/** |
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* RandomForest algorithm is usable *only* with DecisionTree |
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* |
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* @param string $classifier |
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* @param array $classifierOptions |
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* @return $this |
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*/ |
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public function setClassifer(string $classifier, array $classifierOptions = []) |
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{ |
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if ($classifier != DecisionTree::class) { |
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throw new \Exception("RandomForest can only use DecisionTree as base classifier"); |
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} |
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return parent::setClassifer($classifier, $classifierOptions); |
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} |
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/** |
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* This will return an array including an importance value for |
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* each column in the given dataset. Importance values for a column |
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* is the average importance of that column in all trees in the forest |
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* |
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* @return array |
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*/ |
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public function getFeatureImportances() |
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{ |
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// Traverse each tree and sum importance of the columns |
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$sum = []; |
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foreach ($this->classifiers as $tree) { |
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/* @var $tree DecisionTree */ |
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$importances = $tree->getFeatureImportances(); |
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foreach ($importances as $column => $importance) { |
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if (array_key_exists($column, $sum)) { |
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$sum[$column] += $importance; |
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} else { |
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$sum[$column] = $importance; |
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} |
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} |
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} |
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// Normalize & sort the importance values |
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$total = array_sum($sum); |
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foreach ($sum as &$importance) { |
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$importance /= $total; |
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} |
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arsort($sum); |
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return $sum; |
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} |
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/** |
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* A string array to represent the columns is given. They are useful |
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* when trying to print some information about the trees such as feature importances |
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* |
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* @param array $names |
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* @return $this |
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*/ |
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public function setColumnNames(array $names) |
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{ |
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$this->columnNames = $names; |
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return $this; |
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} |
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/** |
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* @param DecisionTree $classifier |
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* @param int $index |
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* @return DecisionTree |
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*/ |
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protected function initSingleClassifier($classifier, $index) |
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{ |
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if (is_float($this->featureSubsetRatio)) { |
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$featureCount = (int)($this->featureSubsetRatio * $this->featureCount); |
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} elseif ($this->featureCount == 'sqrt') { |
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$featureCount = (int)sqrt($this->featureCount) + 1; |
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} else { |
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$featureCount = (int)log($this->featureCount, 2) + 1; |
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} |
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if ($featureCount >= $this->featureCount) { |
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$featureCount = $this->featureCount; |
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} |
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if ($this->columnNames === null) { |
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$this->columnNames = range(0, $this->featureCount - 1); |
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} |
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return $classifier |
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->setColumnNames($this->columnNames) |
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->setNumFeatures($featureCount); |
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} |
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} |
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php-ai /
php-ml
