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<?php |
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declare(strict_types=1); |
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namespace Phpml\Classification; |
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use Phpml\Exception\InvalidArgumentException; |
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use Phpml\Helper\Predictable; |
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use Phpml\Helper\Trainable; |
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use Phpml\Math\Statistic\Mean; |
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use Phpml\Classification\DecisionTree\DecisionTreeLeaf; |
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class DecisionTree implements Classifier |
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{ |
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use Trainable, Predictable; |
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const CONTINUOUS = 1; |
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const NOMINAL = 2; |
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/** |
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* @var array |
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*/ |
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protected $columnTypes = []; |
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/** |
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* @var array |
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*/ |
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private $labels = []; |
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/** |
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* @var int |
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*/ |
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private $featureCount = 0; |
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/** |
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* @var DecisionTreeLeaf |
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*/ |
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protected $tree = null; |
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/** |
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* @var int |
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*/ |
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protected $maxDepth; |
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public static $categoricalColumnMinimumUniqueValueCount = 0.2; |
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/** |
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* @var int |
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*/ |
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public $actualDepth = 0; |
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/** |
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* @var int |
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*/ |
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private $numUsableFeatures = 0; |
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/** |
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* @var array |
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*/ |
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private $selectedFeatures; |
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/** |
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* @var array |
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*/ |
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private $featureImportances = null; |
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/** |
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* |
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* @var array |
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*/ |
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private $columnNames = null; |
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/** |
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* @param int $maxDepth |
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*/ |
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public function __construct(int $maxDepth = 10) |
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{ |
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$this->maxDepth = $maxDepth; |
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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 $targets) |
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{ |
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$this->samples = array_merge($this->samples, $samples); |
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$this->targets = array_merge($this->targets, $targets); |
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$this->featureCount = count($this->samples[0]); |
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if (count($this->columnTypes) != $this->featureCount) { |
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$this->columnTypes = self::getColumnTypes($this->samples); |
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} else { |
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foreach (self::getColumnTypes($this->samples) as $key => $value) { |
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if (is_null($this->columnTypes[$key])) { |
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$this->columnTypes[$key] = $value; |
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} |
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} |
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} |
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unset($key, $value); |
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$this->labels = array_keys(array_count_values($this->targets)); |
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$this->tree = $this->getSplitLeaf(range(0, count($this->samples) - 1)); |
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// Each time the tree is trained, feature importances are reset so that |
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// we will have to compute it again depending on the new data |
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$this->featureImportances = null; |
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// If column names are given or computed before, then there is no |
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// need to init it and accidentally remove the previous given names |
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if ($this->columnNames === null) { |
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$this->columnNames = range(0, $this->featureCount - 1); |
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} elseif (count($this->columnNames) > $this->featureCount) { |
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$this->columnNames = array_slice($this->columnNames, 0, $this->featureCount); |
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} elseif (count($this->columnNames) < $this->featureCount) { |
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$this->columnNames = array_merge($this->columnNames, |
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range(count($this->columnNames), $this->featureCount - 1) |
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); |
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} |
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} |
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/** |
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* @param array $samples |
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* |
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* @return array |
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*/ |
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public static function getColumnTypes(array $samples) : array |
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{ |
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$types = []; |
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$featureCount = count($samples[0]); |
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for ($i = 0; $i < $featureCount; ++$i) { |
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$values = array_column($samples, $i); |
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$isCategorical = self::isCategoricalColumn($values); |
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$types[] = $isCategorical ? self::NOMINAL : self::CONTINUOUS; |
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} |
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return $types; |
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} |
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/** |
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* @param array $records |
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* @param int $depth |
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* |
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* @return DecisionTreeLeaf |
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*/ |
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protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf |
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{ |
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$split = $this->getBestSplit($records); |
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$split->level = $depth; |
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if ($this->actualDepth < $depth) { |
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$this->actualDepth = $depth; |
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} |
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// Traverse all records to see if all records belong to the same class, |
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// otherwise group the records so that we can classify the leaf |
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// in case maximum depth is reached |
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$leftRecords = []; |
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$rightRecords= []; |
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$remainingTargets = []; |
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$prevRecord = null; |
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$allSame = true; |
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foreach ($records as $recordNo) { |
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// Check if the previous record is the same with the current one |
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$record = $this->samples[$recordNo]; |
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if ($prevRecord && $prevRecord != $record) { |
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$allSame = false; |
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} |
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$prevRecord = $record; |
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// According to the split criteron, this record will |
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// belong to either left or the right side in the next split |
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if ($split->evaluate($record)) { |
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$leftRecords[] = $recordNo; |
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} else { |
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$rightRecords[]= $recordNo; |
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} |
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// Group remaining targets |
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$target = $this->targets[$recordNo]; |
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if (!array_key_exists($target, $remainingTargets)) { |
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$remainingTargets[$target] = 1; |
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} else { |
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++$remainingTargets[$target]; |
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} |
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} |
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if ($allSame || $depth >= $this->maxDepth || count($remainingTargets) === 1) { |
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$split->isTerminal = 1; |
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arsort($remainingTargets); |
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$split->classValue = key($remainingTargets); |
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} else { |
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if ($leftRecords) { |
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$split->leftLeaf = $this->getSplitLeaf($leftRecords, $depth + 1); |
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} |
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if ($rightRecords) { |
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$split->rightLeaf= $this->getSplitLeaf($rightRecords, $depth + 1); |
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} |
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} |
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return $split; |
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} |
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/** |
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* @param array $records |
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* |
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* @return DecisionTreeLeaf|null |
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*/ |
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protected function getBestSplit(array $records) : DecisionTreeLeaf |
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{ |
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$targets = array_intersect_key($this->targets, array_flip($records)); |
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$samples = array_intersect_key($this->samples, array_flip($records)); |
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$samples = array_combine($records, $this->preprocess($samples)); |
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$bestGiniVal = 1; |
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$bestSplit = null; |
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$features = $this->getSelectedFeatures(); |
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foreach ($features as $i) { |
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$colValues = []; |
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foreach ($samples as $index => $row) { |
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if (!is_null($row[$i])) { |
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$colValues[$index] = $row[$i]; |
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} |
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} |
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$counts = self::arrayCountValues($colValues); |
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arsort($counts); |
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$baseValue = key($counts); |
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$gini = $this->getGiniIndex($baseValue, $colValues, $targets); |
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if (!is_null($gini) and (is_null($bestSplit) or ($bestGiniVal > $gini))) { |
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$split = new DecisionTreeLeaf(); |
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$split->value = $baseValue; |
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$split->giniIndex = $gini; |
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$split->columnIndex = $i; |
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$split->isContinuous = $this->columnTypes[$i] == self::CONTINUOUS; |
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$split->records = $records; |
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// If a numeric column is to be selected, then |
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// the original numeric value and the selected operator |
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// will also be saved into the leaf for future access |
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if ($this->columnTypes[$i] == self::CONTINUOUS) { |
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$matches = []; |
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preg_match("/^([<>=]{1,2})\s*(.*)/", strval($split->value), $matches); |
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$split->operator = $matches[1]; |
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$split->numericValue = floatval($matches[2]); |
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} |
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$bestSplit = $split; |
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$bestGiniVal = $gini; |
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} |
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} |
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return $bestSplit; |
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} |
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/** |
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* Returns available features/columns to the tree for the decision making |
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* process. <br> |
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* |
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* If a number is given with setNumFeatures() method, then a random selection |
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* of features up to this number is returned. <br> |
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* |
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* If some features are manually selected by use of setSelectedFeatures(), |
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* then only these features are returned <br> |
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* |
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* If any of above methods were not called beforehand, then all features |
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* are returned by default. |
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* |
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* @return array |
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*/ |
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protected function getSelectedFeatures() : array |
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{ |
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$allFeatures = range(0, $this->featureCount - 1); |
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if ($this->numUsableFeatures === 0 && !$this->selectedFeatures) { |
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return $allFeatures; |
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} |
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if ($this->selectedFeatures) { |
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return $this->selectedFeatures; |
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} |
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$numFeatures = $this->numUsableFeatures; |
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if ($numFeatures > $this->featureCount) { |
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$numFeatures = $this->featureCount; |
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} |
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shuffle($allFeatures); |
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$selectedFeatures = array_slice($allFeatures, 0, $numFeatures, false); |
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sort($selectedFeatures); |
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return $selectedFeatures; |
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} |
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/** |
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* @param mixed $baseValue |
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* @param array $colValues |
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* @param array $targets |
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* |
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* @return float |
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*/ |
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public function getGiniIndex($baseValue, array $colValues, array $targets) : float |
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{ |
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if (empty($colValues)) { |
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return 1; |
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} |
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$countMatrix = []; |
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foreach ($this->labels as $label) { |
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$countMatrix[$label] = [0, 0]; |
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} |
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foreach ($colValues as $index => $value) { |
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$label = $targets[$index]; |
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$rowIndex = $value === $baseValue ? 0 : 1; |
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++$countMatrix[$label][$rowIndex]; |
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} |
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$giniParts = [0, 0]; |
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for ($i = 0; $i <= 1; ++$i) { |
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$part = 0; |
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$sum = array_sum(array_column($countMatrix, $i)); |
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if ($sum > 0) { |
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foreach ($this->labels as $label) { |
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$part += pow($countMatrix[$label][$i] / floatval($sum), 2); |
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} |
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} |
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$giniParts[$i] = (1 - $part) * $sum; |
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} |
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return array_sum($giniParts) / count($colValues); |
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} |
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/** |
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* @param array $samples |
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* |
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* @return array |
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*/ |
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protected function preprocess(array $samples) : array |
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{ |
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// Detect and convert continuous data column values into |
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// discrete values by using the median as a threshold value |
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$columns = []; |
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for ($i = 0; $i < $this->featureCount; ++$i) { |
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$values = array_column($samples, $i); |
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if ($this->columnTypes[$i] == self::CONTINUOUS) { |
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$median = Mean::median($values); |
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foreach ($values as &$value) { |
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if ($value <= $median) { |
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$value = "<= $median"; |
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} else { |
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$value = "> $median"; |
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} |
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} |
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} |
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$columns[] = $values; |
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} |
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// Below method is a strange yet very simple & efficient method |
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// to get the transpose of a 2D array |
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return array_map(null, ...$columns); |
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} |
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/** |
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* @param array $columnValues |
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* |
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* @return bool |
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*/ |
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protected static function isCategoricalColumn(array $columnValues) : bool |
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{ |
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$count = count($columnValues); |
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|
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// There are two main indicators that *may* show whether a |
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// column is composed of discrete set of values: |
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// 1- Column may contain string values and non-float values |
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// 2- Number of unique values in the column is only a small fraction of |
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// all values in that column (Lower than or equal to %20 of all values) |
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$numericValues = array_filter($columnValues, 'is_numeric'); |
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$floatValues = array_filter($columnValues, 'is_float'); |
374
|
|
|
$nullCount = count(array_filter($columnValues, 'is_null')); |
375
|
|
|
if (!empty($floatValues)) { |
376
|
|
|
return false; |
377
|
|
|
} |
378
|
|
|
|
379
|
|
|
if (count($numericValues) !== $count - $nullCount) { |
380
|
|
|
return true; |
381
|
|
|
} |
382
|
|
|
|
383
|
|
|
$distinctValuesCount = count(self::arrayCountValues($columnValues)); |
384
|
|
|
|
385
|
|
|
return $distinctValuesCount <= ($count - $nullCount) * self::$categoricalColumnMinimumUniqueValueCount; |
386
|
|
|
} |
387
|
|
|
|
388
|
|
|
/** |
389
|
|
|
* This method is used to set number of columns to be used |
390
|
|
|
* when deciding a split at an internal node of the tree. <br> |
391
|
|
|
* If the value is given 0, then all features are used (default behaviour), |
392
|
|
|
* otherwise the given value will be used as a maximum for number of columns |
393
|
|
|
* randomly selected for each split operation. |
394
|
|
|
* |
395
|
|
|
* @param int $numFeatures |
396
|
|
|
* |
397
|
|
|
* @return $this |
398
|
|
|
* |
399
|
|
|
* @throws InvalidArgumentException |
400
|
|
|
*/ |
401
|
|
|
public function setNumFeatures(int $numFeatures) |
402
|
|
|
{ |
403
|
|
|
if ($numFeatures < 0) { |
404
|
|
|
throw new InvalidArgumentException('Selected column count should be greater or equal to zero'); |
405
|
|
|
} |
406
|
|
|
|
407
|
|
|
$this->numUsableFeatures = $numFeatures; |
408
|
|
|
|
409
|
|
|
return $this; |
410
|
|
|
} |
411
|
|
|
|
412
|
|
|
/** |
413
|
|
|
* Used to set predefined features to consider while deciding which column to use for a split |
414
|
|
|
* |
415
|
|
|
* @param array $selectedFeatures |
416
|
|
|
*/ |
417
|
|
|
protected function setSelectedFeatures(array $selectedFeatures) |
418
|
|
|
{ |
419
|
|
|
$this->selectedFeatures = $selectedFeatures; |
420
|
|
|
} |
421
|
|
|
|
422
|
|
|
/** |
423
|
|
|
* A string array to represent columns. Useful when HTML output or |
424
|
|
|
* column importances are desired to be inspected. |
425
|
|
|
* |
426
|
|
|
* @param array $names |
427
|
|
|
* |
428
|
|
|
* @return $this |
429
|
|
|
* |
430
|
|
|
* @throws InvalidArgumentException |
431
|
|
|
*/ |
432
|
|
|
public function setColumnNames(array $names) |
433
|
|
|
{ |
434
|
|
|
if ($this->featureCount !== 0 && count($names) !== $this->featureCount) { |
435
|
|
|
throw new InvalidArgumentException(sprintf('Length of the given array should be equal to feature count %s', $this->featureCount)); |
436
|
|
|
} |
437
|
|
|
|
438
|
|
|
$this->columnNames = $names; |
439
|
|
|
|
440
|
|
|
return $this; |
441
|
|
|
} |
442
|
|
|
|
443
|
|
|
/** |
444
|
|
|
* @return string |
445
|
|
|
*/ |
446
|
|
|
public function getHtml() |
447
|
|
|
{ |
448
|
|
|
return $this->tree->getHTML($this->columnNames); |
449
|
|
|
} |
450
|
|
|
|
451
|
|
|
/** |
452
|
|
|
* This will return an array including an importance value for |
453
|
|
|
* each column in the given dataset. The importance values are |
454
|
|
|
* normalized and their total makes 1.<br/> |
455
|
|
|
* |
456
|
|
|
* @return array |
457
|
|
|
*/ |
458
|
|
|
public function getFeatureImportances() |
459
|
|
|
{ |
460
|
|
|
if ($this->featureImportances !== null) { |
461
|
|
|
return $this->featureImportances; |
462
|
|
|
} |
463
|
|
|
|
464
|
|
|
$sampleCount = count($this->samples); |
465
|
|
|
$this->featureImportances = []; |
466
|
|
|
foreach ($this->columnNames as $column => $columnName) { |
467
|
|
|
$nodes = $this->getSplitNodesByColumn($column, $this->tree); |
468
|
|
|
|
469
|
|
|
$importance = 0; |
470
|
|
|
foreach ($nodes as $node) { |
471
|
|
|
$importance += $node->getNodeImpurityDecrease($sampleCount); |
472
|
|
|
} |
473
|
|
|
|
474
|
|
|
$this->featureImportances[$columnName] = $importance; |
475
|
|
|
} |
476
|
|
|
|
477
|
|
|
// Normalize & sort the importances |
478
|
|
|
$total = array_sum($this->featureImportances); |
479
|
|
|
if ($total > 0) { |
480
|
|
|
foreach ($this->featureImportances as &$importance) { |
481
|
|
|
$importance /= $total; |
482
|
|
|
} |
483
|
|
|
arsort($this->featureImportances); |
484
|
|
|
} |
485
|
|
|
|
486
|
|
|
return $this->featureImportances; |
487
|
|
|
} |
488
|
|
|
|
489
|
|
|
/** |
490
|
|
|
* Collects and returns an array of internal nodes that use the given |
491
|
|
|
* column as a split criterion |
492
|
|
|
* |
493
|
|
|
* @param int $column |
494
|
|
|
* @param DecisionTreeLeaf $node |
495
|
|
|
* |
496
|
|
|
* @return array |
497
|
|
|
*/ |
498
|
|
|
protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) : array |
499
|
|
|
{ |
500
|
|
|
if (!$node || $node->isTerminal) { |
501
|
|
|
return []; |
502
|
|
|
} |
503
|
|
|
|
504
|
|
|
$nodes = []; |
505
|
|
|
if ($node->columnIndex === $column) { |
506
|
|
|
$nodes[] = $node; |
507
|
|
|
} |
508
|
|
|
|
509
|
|
|
$lNodes = []; |
510
|
|
|
$rNodes = []; |
511
|
|
|
if ($node->leftLeaf) { |
512
|
|
|
$lNodes = $this->getSplitNodesByColumn($column, $node->leftLeaf); |
513
|
|
|
} |
514
|
|
|
|
515
|
|
|
if ($node->rightLeaf) { |
516
|
|
|
$rNodes = $this->getSplitNodesByColumn($column, $node->rightLeaf); |
517
|
|
|
} |
518
|
|
|
|
519
|
|
|
$nodes = array_merge($nodes, $lNodes, $rNodes); |
520
|
|
|
|
521
|
|
|
return $nodes; |
522
|
|
|
} |
523
|
|
|
|
524
|
|
|
/** |
525
|
|
|
* @param array $sample |
526
|
|
|
* |
527
|
|
|
* @return mixed |
528
|
|
|
*/ |
529
|
|
|
protected function predictSample(array $sample) |
530
|
|
|
{ |
531
|
|
|
$node = $this->tree; |
532
|
|
|
do { |
533
|
|
|
if ($node->isTerminal) { |
534
|
|
|
break; |
535
|
|
|
} |
536
|
|
|
|
537
|
|
|
if ($node->evaluate($sample)) { |
538
|
|
|
$node = $node->leftLeaf; |
539
|
|
|
} else { |
540
|
|
|
$node = $node->rightLeaf; |
541
|
|
|
} |
542
|
|
|
} while ($node); |
543
|
|
|
|
544
|
|
|
return $node ? $node->classValue : $this->labels[0]; |
545
|
|
|
} |
546
|
|
|
|
547
|
|
|
/** |
548
|
|
|
* @return integer[]|null[] |
549
|
|
|
*/ |
550
|
|
|
public function getInstanceColumnTypes() { |
551
|
|
|
return $this->columnTypes; |
552
|
|
|
} |
553
|
|
|
|
554
|
|
|
/** |
555
|
|
|
* @param integer[]|null[] $columnTypes |
556
|
|
|
*/ |
557
|
|
|
public function setInstanceColumnTypes(array $columnTypes) { |
558
|
|
|
$this->columnTypes = $columnTypes; |
559
|
|
|
} |
560
|
|
|
|
561
|
|
|
/** |
562
|
|
|
* @param array $values |
563
|
|
|
* |
564
|
|
|
* @return array |
565
|
|
|
*/ |
566
|
|
|
protected static function arrayCountValues(array $values) { |
567
|
|
|
$raw = []; |
568
|
|
|
foreach ($values as &$value) { |
569
|
|
|
if (!is_null($value) and !is_float($value)) { |
|
|
|
|
570
|
|
|
$raw[] = $value; |
571
|
|
|
} |
572
|
|
|
} |
573
|
|
|
|
574
|
|
|
return array_count_values($raw); |
575
|
|
|
} |
576
|
|
|
} |
577
|
|
|
|
Our type inference engine has found an assignment to a property that is incompatible with the declared type of that property.
Either this assignment is in error or the assigned type should be added to the documentation/type hint for that property..