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Add typehints to DecisionTree
This commit is contained in:
Arkadiusz Kondas
parent
01bb82a2a7
commit
c6fbb83573
@ -4,6 +4,7 @@ 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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@ -13,7 +14,7 @@ class DecisionTree implements Classifier
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{
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use Trainable, Predictable;
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const CONTINUOS = 1;
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const CONTINUOUS = 1;
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const NOMINAL = 2;
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/**
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@ -70,7 +71,7 @@ class DecisionTree implements Classifier
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/**
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* @param int $maxDepth
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*/
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public function __construct($maxDepth = 10)
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public function __construct(int $maxDepth = 10)
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{
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$this->maxDepth = $maxDepth;
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}
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@ -85,7 +86,7 @@ class DecisionTree implements Classifier
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$this->targets = array_merge($this->targets, $targets);
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$this->featureCount = count($this->samples[0]);
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$this->columnTypes = $this->getColumnTypes($this->samples);
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$this->columnTypes = self::getColumnTypes($this->samples);
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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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@ -105,23 +106,29 @@ class DecisionTree implements Classifier
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}
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}
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public static function getColumnTypes(array $samples)
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/**
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* @param array $samples
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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[] = $isCategorical ? self::NOMINAL : self::CONTINUOS;
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$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 null|array $records
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* @param array $records
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* @param int $depth
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* @return DecisionTreeLeaf
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*/
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protected function getSplitLeaf($records, $depth = 0)
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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 = $depth;
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@ -163,7 +170,7 @@ class DecisionTree implements Classifier
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}
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}
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if (count($remainingTargets) == 1 || $allSame || $depth >= $this->maxDepth) {
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if ($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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@ -175,14 +182,15 @@ class DecisionTree implements Classifier
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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 $records
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* @return DecisionTreeLeaf[]
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* @return DecisionTreeLeaf
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*/
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protected function getBestSplit($records)
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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($records));
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@ -199,18 +207,18 @@ class DecisionTree implements Classifier
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arsort($counts);
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$baseValue = key($counts);
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$gini = $this->getGiniIndex($baseValue, $colValues, $targets);
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if ($bestSplit == null || $bestGiniVal > $gini) {
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if ($bestSplit === null || $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::CONTINUOS;
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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[$i] == self::CONTINUOS) {
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if ($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 = $matches[1];
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@ -221,6 +229,7 @@ class DecisionTree implements Classifier
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$bestGiniVal = $gini;
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}
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}
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return $bestSplit;
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}
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@ -239,10 +248,10 @@ class DecisionTree implements Classifier
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*
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* @return array
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*/
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protected function getSelectedFeatures()
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protected function getSelectedFeatures() : array
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{
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$allFeatures = range(0, $this->featureCount - 1);
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if ($this->numUsableFeatures == 0 && ! $this->selectedFeatures) {
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if ($this->numUsableFeatures === 0 && ! $this->selectedFeatures) {
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return $allFeatures;
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}
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@ -262,11 +271,12 @@ class DecisionTree implements Classifier
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}
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/**
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* @param string $baseValue
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* @param $baseValue
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* @param array $colValues
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* @param array $targets
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* @return float
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*/
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public function getGiniIndex($baseValue, $colValues, $targets)
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public function getGiniIndex($baseValue, array $colValues, array $targets) : float
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{
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$countMatrix = [];
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foreach ($this->labels as $label) {
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@ -274,7 +284,7 @@ class DecisionTree implements Classifier
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}
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foreach ($colValues as $index => $value) {
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$label = $targets[$index];
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$rowIndex = $value == $baseValue ? 0 : 1;
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$rowIndex = $value === $baseValue ? 0 : 1;
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$countMatrix[$label][$rowIndex]++;
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}
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$giniParts = [0, 0];
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@ -288,6 +298,7 @@ class DecisionTree implements Classifier
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}
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$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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@ -295,14 +306,14 @@ class DecisionTree implements Classifier
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* @param array $samples
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* @return array
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*/
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protected function preprocess(array $samples)
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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[$i] == self::CONTINUOS) {
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if ($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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@ -323,7 +334,7 @@ class DecisionTree implements Classifier
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* @param array $columnValues
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* @return bool
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*/
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protected static function isCategoricalColumn(array $columnValues)
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protected static function isCategoricalColumn(array $columnValues) : bool
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{
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$count = count($columnValues);
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@ -337,15 +348,13 @@ class DecisionTree implements Classifier
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if ($floatValues) {
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return false;
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}
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if (count($numericValues) != $count) {
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if (count($numericValues) !== $count) {
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return true;
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}
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$distinctValues = array_count_values($columnValues);
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if (count($distinctValues) <= $count / 5) {
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return true;
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}
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return false;
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return count($distinctValues) <= $count / 5;
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}
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/**
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@ -357,12 +366,12 @@ class DecisionTree implements Classifier
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*
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* @param int $numFeatures
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* @return $this
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* @throws Exception
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* @throws InvalidArgumentException
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*/
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public function setNumFeatures(int $numFeatures)
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{
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if ($numFeatures < 0) {
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throw new \Exception("Selected column count should be greater or equal to zero");
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throw new InvalidArgumentException('Selected column count should be greater or equal to zero');
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}
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$this->numUsableFeatures = $numFeatures;
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@ -386,11 +395,12 @@ class DecisionTree implements Classifier
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*
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* @param array $names
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* @return $this
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* @throws InvalidArgumentException
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*/
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public function setColumnNames(array $names)
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{
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if ($this->featureCount != 0 && count($names) != $this->featureCount) {
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throw new \Exception("Length of the given array should be equal to feature count ($this->featureCount)");
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if ($this->featureCount !== 0 && count($names) !== $this->featureCount) {
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throw new InvalidArgumentException(sprintf('Length of the given array should be equal to feature count %s', $this->featureCount));
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}
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$this->columnNames = $names;
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@ -411,7 +421,6 @@ class DecisionTree implements Classifier
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* each column in the given dataset. The importance values are
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* normalized and their total makes 1.<br/>
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*
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* @param array $labels
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* @return array
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*/
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public function getFeatureImportances()
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@ -447,22 +456,20 @@ class DecisionTree implements Classifier
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/**
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* Collects and returns an array of internal nodes that use the given
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* column as a split criteron
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* column as a split criterion
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*
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* @param int $column
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* @param DecisionTreeLeaf
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* @param array $collected
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*
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* @param DecisionTreeLeaf $node
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* @return array
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*/
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protected function getSplitNodesByColumn($column, DecisionTreeLeaf $node)
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protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) : array
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{
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if (!$node || $node->isTerminal) {
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return [];
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}
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$nodes = [];
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if ($node->columnIndex == $column) {
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if ($node->columnIndex === $column) {
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$nodes[] = $node;
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}
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@ -135,7 +135,7 @@ class DecisionStump extends WeightedClassifier
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'prob' => [], 'column' => 0,
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'trainingErrorRate' => 1.0];
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foreach ($columns as $col) {
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if ($this->columnTypes[$col] == DecisionTree::CONTINUOS) {
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if ($this->columnTypes[$col] == DecisionTree::CONTINUOUS) {
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$split = $this->getBestNumericalSplit($col);
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} else {
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$split = $this->getBestNominalSplit($col);
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@ -6,7 +6,10 @@ namespace Phpml\Classification;
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abstract class WeightedClassifier implements Classifier
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{
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protected $weights = null;
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/**
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* @var array
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*/
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protected $weights;
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/**
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* Sets the array including a weight for each sample
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