Add typehints to DecisionTree

src/Phpml/Classification/DecisionTree.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\Classification;
 
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\Helper\Predictable;
 use Phpml\Helper\Trainable;
 use Phpml\Math\Statistic\Mean;
@@ -13,7 +14,7 @@ class DecisionTree implements Classifier
 {
     use Trainable, Predictable;
 
-    const CONTINUOS = 1;
+    const CONTINUOUS = 1;
     const NOMINAL = 2;
 
     /**
@@ -70,7 +71,7 @@ class DecisionTree implements Classifier
     /**
      * @param int $maxDepth
      */
-    public function __construct($maxDepth = 10)
+    public function __construct(int $maxDepth = 10)
     {
         $this->maxDepth = $maxDepth;
     }
@@ -85,7 +86,7 @@ class DecisionTree implements Classifier
         $this->targets = array_merge($this->targets, $targets);
 
         $this->featureCount = count($this->samples[0]);
-        $this->columnTypes = $this->getColumnTypes($this->samples);
+        $this->columnTypes = self::getColumnTypes($this->samples);
         $this->labels = array_keys(array_count_values($this->targets));
         $this->tree = $this->getSplitLeaf(range(0, count($this->samples) - 1));
 
@@ -105,23 +106,29 @@ class DecisionTree implements Classifier
         }
     }
 
-    public static function getColumnTypes(array $samples)
+    /**
+     * @param array $samples
+     * @return array
+     */
+    public static function getColumnTypes(array $samples) : array
     {
         $types = [];
         $featureCount = count($samples[0]);
         for ($i=0; $i < $featureCount; $i++) {
             $values = array_column($samples, $i);
             $isCategorical = self::isCategoricalColumn($values);
-            $types[] = $isCategorical ? self::NOMINAL : self::CONTINUOS;
+            $types[] = $isCategorical ? self::NOMINAL : self::CONTINUOUS;
         }
+
         return $types;
     }
 
     /**
-     * @param null|array $records
+     * @param array $records
+     * @param int $depth
      * @return DecisionTreeLeaf
      */
-    protected function getSplitLeaf($records, $depth = 0)
+    protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf
     {
         $split = $this->getBestSplit($records);
         $split->level = $depth;
@@ -163,7 +170,7 @@ class DecisionTree implements Classifier
             }
         }
 
-        if (count($remainingTargets) == 1 || $allSame || $depth >= $this->maxDepth) {
+        if ($allSame || $depth >= $this->maxDepth || count($remainingTargets) === 1) {
             $split->isTerminal = 1;
             arsort($remainingTargets);
             $split->classValue = key($remainingTargets);
@@ -175,14 +182,15 @@ class DecisionTree implements Classifier
                 $split->rightLeaf= $this->getSplitLeaf($rightRecords, $depth + 1);
             }
         }
+
         return $split;
     }
 
     /**
      * @param array $records
-     * @return DecisionTreeLeaf[]
+     * @return DecisionTreeLeaf
      */
-    protected function getBestSplit($records)
+    protected function getBestSplit(array $records) : DecisionTreeLeaf
     {
         $targets = array_intersect_key($this->targets, array_flip($records));
         $samples = array_intersect_key($this->samples, array_flip($records));
@@ -199,18 +207,18 @@ class DecisionTree implements Classifier
             arsort($counts);
             $baseValue = key($counts);
             $gini = $this->getGiniIndex($baseValue, $colValues, $targets);
-            if ($bestSplit == null || $bestGiniVal > $gini) {
+            if ($bestSplit === null || $bestGiniVal > $gini) {
                 $split = new DecisionTreeLeaf();
                 $split->value = $baseValue;
                 $split->giniIndex = $gini;
                 $split->columnIndex = $i;
-                $split->isContinuous = $this->columnTypes[$i] == self::CONTINUOS;
+                $split->isContinuous = $this->columnTypes[$i] == self::CONTINUOUS;
                 $split->records = $records;
 
                 // If a numeric column is to be selected, then
                 // the original numeric value and the selected operator
                 // will also be saved into the leaf for future access
-                if ($this->columnTypes[$i] == self::CONTINUOS) {
+                if ($this->columnTypes[$i] == self::CONTINUOUS) {
                     $matches = [];
                     preg_match("/^([<>=]{1,2})\s*(.*)/", strval($split->value), $matches);
                     $split->operator = $matches[1];
@@ -221,6 +229,7 @@ class DecisionTree implements Classifier
                 $bestGiniVal = $gini;
             }
         }
+
         return $bestSplit;
     }
 
@@ -239,10 +248,10 @@ class DecisionTree implements Classifier
      *
      * @return array
      */
-    protected function getSelectedFeatures()
+    protected function getSelectedFeatures() : array
     {
         $allFeatures = range(0, $this->featureCount - 1);
-        if ($this->numUsableFeatures == 0 && ! $this->selectedFeatures) {
+        if ($this->numUsableFeatures === 0 && ! $this->selectedFeatures) {
             return $allFeatures;
         }
 
@@ -262,11 +271,12 @@ class DecisionTree implements Classifier
     }
 
     /**
-     * @param string $baseValue
+     * @param $baseValue
      * @param array $colValues
      * @param array $targets
+     * @return float
      */
-    public function getGiniIndex($baseValue, $colValues, $targets)
+    public function getGiniIndex($baseValue, array $colValues, array $targets) : float
     {
         $countMatrix = [];
         foreach ($this->labels as $label) {
@@ -274,7 +284,7 @@ class DecisionTree implements Classifier
         }
         foreach ($colValues as $index => $value) {
             $label = $targets[$index];
-            $rowIndex = $value == $baseValue ? 0 : 1;
+            $rowIndex = $value === $baseValue ? 0 : 1;
             $countMatrix[$label][$rowIndex]++;
         }
         $giniParts = [0, 0];
@@ -288,6 +298,7 @@ class DecisionTree implements Classifier
             }
             $giniParts[$i] = (1 - $part) * $sum;
         }
+
         return array_sum($giniParts) / count($colValues);
     }
 
@@ -295,14 +306,14 @@ class DecisionTree implements Classifier
      * @param array $samples
      * @return array
      */
-    protected function preprocess(array $samples)
+    protected function preprocess(array $samples) : array
     {
         // Detect and convert continuous data column values into
         // discrete values by using the median as a threshold value
         $columns = [];
         for ($i=0; $i<$this->featureCount; $i++) {
             $values = array_column($samples, $i);
-            if ($this->columnTypes[$i] == self::CONTINUOS) {
+            if ($this->columnTypes[$i] == self::CONTINUOUS) {
                 $median = Mean::median($values);
                 foreach ($values as &$value) {
                     if ($value <= $median) {
@@ -323,7 +334,7 @@ class DecisionTree implements Classifier
      * @param array $columnValues
      * @return bool
      */
-    protected static function isCategoricalColumn(array $columnValues)
+    protected static function isCategoricalColumn(array $columnValues) : bool
     {
         $count = count($columnValues);
 
@@ -337,15 +348,13 @@ class DecisionTree implements Classifier
         if ($floatValues) {
             return false;
         }
-        if (count($numericValues) != $count) {
+        if (count($numericValues) !== $count) {
             return true;
         }
 
         $distinctValues = array_count_values($columnValues);
-        if (count($distinctValues) <= $count / 5) {
+
-            return true;
+        return count($distinctValues) <= $count / 5;
-        }
-        return false;
     }
 
     /**
@@ -357,12 +366,12 @@ class DecisionTree implements Classifier
      *
      * @param int $numFeatures
      * @return $this
-     * @throws Exception
+     * @throws InvalidArgumentException
      */
     public function setNumFeatures(int $numFeatures)
     {
         if ($numFeatures < 0) {
-            throw new \Exception("Selected column count should be greater or equal to zero");
+            throw new InvalidArgumentException('Selected column count should be greater or equal to zero');
         }
 
         $this->numUsableFeatures = $numFeatures;
@@ -386,11 +395,12 @@ class DecisionTree implements Classifier
      *
      * @param array $names
      * @return $this
+     * @throws InvalidArgumentException
      */
     public function setColumnNames(array $names)
     {
-        if ($this->featureCount != 0 && count($names) != $this->featureCount) {
+        if ($this->featureCount !== 0 && count($names) !== $this->featureCount) {
-            throw new \Exception("Length of the given array should be equal to feature count ($this->featureCount)");
+            throw new InvalidArgumentException(sprintf('Length of the given array should be equal to feature count %s', $this->featureCount));
         }
 
         $this->columnNames = $names;
@@ -411,7 +421,6 @@ class DecisionTree implements Classifier
      * each column in the given dataset. The importance values are
      * normalized and their total makes 1.<br/>
      *
-     * @param array $labels
      * @return array
      */
     public function getFeatureImportances()
@@ -447,22 +456,20 @@ class DecisionTree implements Classifier
 
     /**
      * Collects and returns an array of internal nodes that use the given
-     * column as a split criteron
+     * column as a split criterion
      *
      * @param int $column
-     * @param DecisionTreeLeaf
+     * @param DecisionTreeLeaf $node
-     * @param array $collected
-     *
      * @return array
      */
-    protected function getSplitNodesByColumn($column, DecisionTreeLeaf $node)
+    protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) : array
     {
         if (!$node || $node->isTerminal) {
             return [];
         }
 
         $nodes = [];
-        if ($node->columnIndex == $column) {
+        if ($node->columnIndex === $column) {
             $nodes[] = $node;
         }
 

src/Phpml/Classification/Linear/DecisionStump.php

@@ -135,7 +135,7 @@ class DecisionStump extends WeightedClassifier
             'prob' => [], 'column' => 0,
             'trainingErrorRate' => 1.0];
         foreach ($columns as $col) {
-            if ($this->columnTypes[$col] == DecisionTree::CONTINUOS) {
+            if ($this->columnTypes[$col] == DecisionTree::CONTINUOUS) {
                 $split = $this->getBestNumericalSplit($col);
             } else {
                 $split = $this->getBestNominalSplit($col);

src/Phpml/Classification/WeightedClassifier.php

@@ -6,7 +6,10 @@ namespace Phpml\Classification;
 
 abstract class WeightedClassifier implements Classifier
 {
-    protected $weights = null;
+    /**
+     * @var array
+     */
+    protected $weights;
 
     /**
      * Sets the array including a weight for each sample