[coding standard] fix imports order and drop unused docs typehints (#…

…145) * fix imports order * drop unused docs typehints, make use of return types where possible
jiangslee · Nov 6, 2017 · f4650c6 · f4650c6
1 parent a077265
commit f4650c6
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Showing 106 changed files with 301 additions and 1,733 deletions.
diff --git a/docs/math/distance.md b/docs/math/distance.md
@@ -94,7 +94,7 @@ class CustomDistance implements Distance
      *
      * @return float
      */
-    public function distance(array $a, array $b): float
+    public function distance(array $a, array $b) : float
     {
         $distance = [];
         $count = count($a);

diff --git a/src/Phpml/Association/Apriori.php b/src/Phpml/Association/Apriori.php
@@ -49,9 +49,6 @@ class Apriori implements Associator
 
     /**
      * Apriori constructor.
-     *
-     * @param float $support
-     * @param float $confidence
      */
     public function __construct(float $support = 0.0, float $confidence = 0.0)
     {
@@ -261,8 +258,6 @@ private function candidates(array $samples) : array
      *
      * @param mixed[] $set
      * @param mixed[] $subset
-     *
-     * @return float
      */
     private function confidence(array $set, array $subset) : float
     {
@@ -276,8 +271,6 @@ private function confidence(array $set, array $subset) : float
      * @see \Phpml\Association\Apriori::samples
      *
      * @param mixed[] $sample
-     *
-     * @return float
      */
     private function support(array $sample) : float
     {
@@ -290,8 +283,6 @@ private function support(array $sample) : float
      * @see \Phpml\Association\Apriori::samples
      *
      * @param mixed[] $sample
-     *
-     * @return int
      */
     private function frequency(array $sample) : int
     {
@@ -307,8 +298,6 @@ private function frequency(array $sample) : int
      *
      * @param mixed[][] $system
      * @param mixed[]   $set
-     *
-     * @return bool
      */
     private function contains(array $system, array $set) : bool
     {
@@ -322,8 +311,6 @@ private function contains(array $system, array $set) : bool
      *
      * @param mixed[] $set
      * @param mixed[] $subset
-     *
-     * @return bool
      */
     private function subset(array $set, array $subset) : bool
     {
@@ -335,8 +322,6 @@ private function subset(array $set, array $subset) : bool
      *
      * @param mixed[] $set1
      * @param mixed[] $set2
-     *
-     * @return bool
      */
     private function equals(array $set1, array $set2) : bool
     {

diff --git a/src/Phpml/Classification/DecisionTree.php b/src/Phpml/Classification/DecisionTree.php
@@ -4,11 +4,11 @@
 
 namespace Phpml\Classification;
 
+use Phpml\Classification\DecisionTree\DecisionTreeLeaf;
 use Phpml\Exception\InvalidArgumentException;
 use Phpml\Helper\Predictable;
 use Phpml\Helper\Trainable;
 use Phpml\Math\Statistic\Mean;
-use Phpml\Classification\DecisionTree\DecisionTreeLeaf;
 
 class DecisionTree implements Classifier
 {
@@ -63,14 +63,10 @@ class DecisionTree implements Classifier
     private $featureImportances = null;
 
     /**
-     *
      * @var array
      */
     private $columnNames = null;
 
-    /**
-     * @param int $maxDepth
-     */
     public function __construct(int $maxDepth = 10)
     {
         $this->maxDepth = $maxDepth;
@@ -129,8 +125,6 @@ public static function getColumnTypes(array $samples) : array
     /**
      * @param array $records
      * @param int   $depth
-     *
-     * @return DecisionTreeLeaf
      */
     protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLeaf
     {
@@ -190,11 +184,6 @@ protected function getSplitLeaf(array $records, int $depth = 0) : DecisionTreeLe
         return $split;
     }
 
-    /**
-     * @param array $records
-     *
-     * @return DecisionTreeLeaf
-     */
     protected function getBestSplit(array $records) : DecisionTreeLeaf
     {
         $targets = array_intersect_key($this->targets, array_flip($records));
@@ -277,10 +266,6 @@ protected function getSelectedFeatures() : array
 
     /**
      * @param mixed $baseValue
-     * @param array $colValues
-     * @param array $targets
-     *
-     * @return float
      */
     public function getGiniIndex($baseValue, array $colValues, array $targets) : float
     {
@@ -342,8 +327,6 @@ protected function preprocess(array $samples) : array
 
     /**
      * @param array $columnValues
-     *
-     * @return bool
      */
     protected static function isCategoricalColumn(array $columnValues) : bool
     {
@@ -376,8 +359,6 @@ protected static function isCategoricalColumn(array $columnValues) : bool
      * otherwise the given value will be used as a maximum for number of columns
      * randomly selected for each split operation.
      *
-     * @param int $numFeatures
-     *
      * @return $this
      *
      * @throws InvalidArgumentException
@@ -395,8 +376,6 @@ public function setNumFeatures(int $numFeatures)
 
     /**
      * Used to set predefined features to consider while deciding which column to use for a split
-     *
-     * @param array $selectedFeatures
      */
     protected function setSelectedFeatures(array $selectedFeatures)
     {
@@ -407,8 +386,6 @@ protected function setSelectedFeatures(array $selectedFeatures)
      * A string array to represent columns. Useful when HTML output or
      * column importances are desired to be inspected.
      *
-     * @param array $names
-     *
      * @return $this
      *
      * @throws InvalidArgumentException
@@ -424,10 +401,7 @@ public function setColumnNames(array $names)
         return $this;
     }
 
-    /**
-     * @return string
-     */
-    public function getHtml()
+    public function getHtml() : string
     {
         return $this->tree->getHTML($this->columnNames);
     }
@@ -436,10 +410,8 @@ public function getHtml()
      * This will return an array including an importance value for
      * each column in the given dataset. The importance values are
      * normalized and their total makes 1.<br/>
-     *
-     * @return array
      */
-    public function getFeatureImportances()
+    public function getFeatureImportances() : array
     {
         if ($this->featureImportances !== null) {
             return $this->featureImportances;
@@ -473,11 +445,6 @@ public function getFeatureImportances()
     /**
      * Collects and returns an array of internal nodes that use the given
      * column as a split criterion
-     *
-     * @param int              $column
-     * @param DecisionTreeLeaf $node
-     *
-     * @return array
      */
     protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) : array
     {
@@ -506,8 +473,6 @@ protected function getSplitNodesByColumn(int $column, DecisionTreeLeaf $node) :
     }
 
     /**
-     * @param array $sample
-     *
      * @return mixed
      */
     protected function predictSample(array $sample)

diff --git a/src/Phpml/Classification/DecisionTree/DecisionTreeLeaf.php b/src/Phpml/Classification/DecisionTree/DecisionTreeLeaf.php
@@ -71,31 +71,22 @@ class DecisionTreeLeaf
      */
     public $level = 0;
 
-    /**
-     * @param array $record
-     *
-     * @return bool
-     */
-    public function evaluate($record)
+    public function evaluate(array $record) : bool
     {
         $recordField = $record[$this->columnIndex];
 
         if ($this->isContinuous) {
             return Comparison::compare((string) $recordField, $this->numericValue, $this->operator);
         }
-        
+
         return $recordField == $this->value;
     }
 
     /**
      * Returns Mean Decrease Impurity (MDI) in the node.
      * For terminal nodes, this value is equal to 0
-     *
-     * @param int $parentRecordCount
-     *
-     * @return float
      */
-    public function getNodeImpurityDecrease(int $parentRecordCount)
+    public function getNodeImpurityDecrease(int $parentRecordCount) : float
     {
         if ($this->isTerminal) {
             return 0.0;
@@ -119,12 +110,8 @@ public function getNodeImpurityDecrease(int $parentRecordCount)
 
     /**
      * Returns HTML representation of the node including children nodes
-     *
-     * @param $columnNames
-     *
-     * @return string
      */
-    public function getHTML($columnNames = null)
+    public function getHTML($columnNames = null) : string
     {
         if ($this->isTerminal) {
             $value = "<b>$this->classValue</b>";
@@ -170,10 +157,8 @@ public function getHTML($columnNames = null)
 
     /**
      * HTML representation of the tree without column names
-     *
-     * @return string
      */
-    public function __toString()
+    public function __toString() : string
     {
         return $this->getHTML();
     }

diff --git a/src/Phpml/Classification/Ensemble/AdaBoost.php b/src/Phpml/Classification/Ensemble/AdaBoost.php
@@ -4,13 +4,13 @@
 
 namespace Phpml\Classification\Ensemble;
 
+use Phpml\Classification\Classifier;
 use Phpml\Classification\Linear\DecisionStump;
 use Phpml\Classification\WeightedClassifier;
-use Phpml\Math\Statistic\Mean;
-use Phpml\Math\Statistic\StandardDeviation;
-use Phpml\Classification\Classifier;
 use Phpml\Helper\Predictable;
 use Phpml\Helper\Trainable;
+use Phpml\Math\Statistic\Mean;
+use Phpml\Math\Statistic\StandardDeviation;
 
 class AdaBoost implements Classifier
 {
@@ -75,8 +75,6 @@ class AdaBoost implements Classifier
      * ADAptive BOOSTing (AdaBoost) is an ensemble algorithm to
      * improve classification performance of 'weak' classifiers such as
      * DecisionStump (default base classifier of AdaBoost).
-     *
-     * @param int $maxIterations
      */
     public function __construct(int $maxIterations = 50)
     {
@@ -85,9 +83,6 @@ public function __construct(int $maxIterations = 50)
 
     /**
      * Sets the base classifier that will be used for boosting (default = DecisionStump)
-     *
-     * @param string $baseClassifier
-     * @param array  $classifierOptions
      */
     public function setBaseClassifier(string $baseClassifier = DecisionStump::class, array $classifierOptions = [])
     {
@@ -96,9 +91,6 @@ public function setBaseClassifier(string $baseClassifier = DecisionStump::class,
     }
 
     /**
-     * @param array $samples
-     * @param array $targets
-     *
      * @throws \Exception
      */
     public function train(array $samples, array $targets)
@@ -143,10 +135,8 @@ public function train(array $samples, array $targets)
     /**
      * Returns the classifier with the lowest error rate with the
      * consideration of current sample weights
-     *
-     * @return Classifier
      */
-    protected function getBestClassifier()
+    protected function getBestClassifier() : Classifier
     {
         $ref = new \ReflectionClass($this->baseClassifier);
         if ($this->classifierOptions) {
@@ -169,10 +159,8 @@ protected function getBestClassifier()
     /**
      * Resamples the dataset in accordance with the weights and
      * returns the new dataset
-     *
-     * @return array
      */
-    protected function resample()
+    protected function resample() : array
     {
         $weights = $this->weights;
         $std = StandardDeviation::population($weights);
@@ -198,12 +186,8 @@ protected function resample()
 
     /**
      * Evaluates the classifier and returns the classification error rate
-     *
-     * @param Classifier $classifier
-     *
-     * @return float
      */
-    protected function evaluateClassifier(Classifier $classifier)
+    protected function evaluateClassifier(Classifier $classifier) : float
     {
         $total = (float) array_sum($this->weights);
         $wrong = 0;
@@ -219,12 +203,8 @@ protected function evaluateClassifier(Classifier $classifier)
 
     /**
      * Calculates alpha of a classifier
-     *
-     * @param float $errorRate
-     *
-     * @return float
      */
-    protected function calculateAlpha(float $errorRate)
+    protected function calculateAlpha(float $errorRate) : float
     {
         if ($errorRate == 0) {
             $errorRate = 1e-10;
@@ -235,9 +215,6 @@ protected function calculateAlpha(float $errorRate)
 
     /**
      * Updates the sample weights
-     *
-     * @param Classifier $classifier
-     * @param float      $alpha
      */
     protected function updateWeights(Classifier $classifier, float $alpha)
     {
@@ -256,8 +233,6 @@ protected function updateWeights(Classifier $classifier, float $alpha)
     }
 
     /**
-     * @param array $sample
-     *
      * @return mixed
      */
     public function predictSample(array $sample)