Update phpstan & easy coding standard (#366)

composer.json

@@ -24,9 +24,9 @@
     },
     "require-dev": {
         "phpbench/phpbench": "^0.14.0",
-        "phpstan/phpstan-phpunit": "^0.10",
+        "phpstan/phpstan-phpunit": "^0.11",
-        "phpstan/phpstan-shim": "^0.10",
+        "phpstan/phpstan-shim": "^0.11",
-        "phpstan/phpstan-strict-rules": "^0.10",
+        "phpstan/phpstan-strict-rules": "^0.11",
         "phpunit/phpunit": "^7.0.0",
         "symplify/coding-standard": "^5.1",
         "symplify/easy-coding-standard": "^5.1"

ecs.yml

@@ -30,14 +30,14 @@ services:
     Symplify\CodingStandard\Fixer\ArrayNotation\StandaloneLineInMultilineArrayFixer: ~
 
 parameters:
-    exclude_checkers:
-        # from strict.neon
-        - 'PhpCsFixer\Fixer\PhpUnit\PhpUnitStrictFixer'
-        - 'PhpCsFixer\Fixer\Strict\StrictComparisonFixer'
-        # personal prefference
-        - 'PhpCsFixer\Fixer\Operator\NotOperatorWithSuccessorSpaceFixer'
-
     skip:
+        # from strict.neon
+        PhpCsFixer\Fixer\PhpUnit\PhpUnitStrictFixer: ~
+        PhpCsFixer\Fixer\Strict\StrictComparisonFixer: ~
+
+        # personal prefference
+        PhpCsFixer\Fixer\Operator\NotOperatorWithSuccessorSpaceFixer: ~
+
         PhpCsFixer\Fixer\Alias\RandomApiMigrationFixer:
             # random_int() breaks code
             - 'src/CrossValidation/RandomSplit.php'
@@ -65,4 +65,4 @@ parameters:
         SlevomatCodingStandard\Sniffs\TypeHints\TypeHintDeclarationSniff.MissingTraversablePropertyTypeHintSpecification: ~
 
         # assignment in "while ($var = ...)" are ok
-        PHP_CodeSniffer\Standards\Generic\Sniffs\CodeAnalysis\AssignmentInConditionSniff.FoundInWhileCondition:
+        PHP_CodeSniffer\Standards\Generic\Sniffs\CodeAnalysis\AssignmentInConditionSniff.FoundInWhileCondition:

phpstan.neon

@@ -7,9 +7,8 @@ parameters:
     ignoreErrors:
         - '#Property Phpml\\Clustering\\KMeans\\Cluster\:\:\$points \(iterable\<Phpml\\Clustering\\KMeans\\Point\>\&SplObjectStorage\) does not accept SplObjectStorage#'
         - '#Phpml\\Dataset\\(.*)Dataset::__construct\(\) does not call parent constructor from Phpml\\Dataset\\ArrayDataset#'
-
+        - '#Variable property access on .+#'
-        # wide range cases
+        - '#Variable method call on .+#'
-        - '#Parameter \#1 \$coordinates of class Phpml\\Clustering\\KMeans\\Point constructor expects array, array<int>\|Phpml\\Clustering\\KMeans\\Point given#'
 
         # probably known value
         - '#Method Phpml\\Classification\\DecisionTree::getBestSplit\(\) should return Phpml\\Classification\\DecisionTree\\DecisionTreeLeaf but returns Phpml\\Classification\\DecisionTree\\DecisionTreeLeaf\|null#'

src/Classification/Ensemble/RandomForest.php

@@ -41,7 +41,7 @@ class RandomForest extends Bagging
      * Default value for the ratio is 'log' which results in log(numFeatures, 2) + 1
      * features to be taken into consideration while selecting subspace of features
      *
-     * @param string|float $ratio
+     * @param mixed $ratio
      */
     public function setFeatureSubsetRatio($ratio): self
     {
@@ -73,7 +73,9 @@ class RandomForest extends Bagging
             throw new InvalidArgumentException('RandomForest can only use DecisionTree as base classifier');
         }
 
-        return parent::setClassifer($classifier, $classifierOptions);
+        parent::setClassifer($classifier, $classifierOptions);
+
+        return $this;
     }
 
     /**
@@ -122,12 +124,16 @@ class RandomForest extends Bagging
     }
 
     /**
-     * @param DecisionTree $classifier
-     *
      * @return DecisionTree
      */
     protected function initSingleClassifier(Classifier $classifier): Classifier
     {
+        if (!$classifier instanceof DecisionTree) {
+            throw new InvalidArgumentException(
+                sprintf('Classifier %s expected, got %s', DecisionTree::class, get_class($classifier))
+            );
+        }
+
         if (is_float($this->featureSubsetRatio)) {
             $featureCount = (int) ($this->featureSubsetRatio * $this->featureCount);
         } elseif ($this->featureSubsetRatio === 'sqrt') {

src/Classification/Linear/LogisticRegression.php

@@ -226,7 +226,7 @@ class LogisticRegression extends Adaline
 
                     $y = $y < 0 ? 0 : 1;
 
-                    $error = ($y - $hX) ** 2;
+                    $error = (($y - $hX) ** 2);
                     $gradient = -($y - $hX) * $hX * (1 - $hX);
 
                     return [$error, $gradient, $penalty];

src/Clustering/FuzzyCMeans.php

@@ -77,9 +77,6 @@ class FuzzyCMeans implements Clusterer
         return $this->membership;
     }
 
-    /**
-     * @param Point[]|int[][] $samples
-     */
     public function cluster(array $samples): array
     {
         // Initialize variables, clusters and membership matrix
@@ -210,7 +207,7 @@ class FuzzyCMeans implements Clusterer
                 $this->samples[$col]
             );
 
-            $val = ($dist1 / $dist2) ** 2.0 / ($this->fuzziness - 1);
+            $val = (($dist1 / $dist2) ** 2.0) / ($this->fuzziness - 1);
             $sum += $val;
         }
 

src/Dataset/SvmDataset.php

@@ -24,7 +24,7 @@ class SvmDataset extends ArrayDataset
         $targets = [];
         $maxIndex = 0;
         while (false !== $line = fgets($handle)) {
-            [$sample, $target, $maxIndex] = self::processLine((string) $line, $maxIndex);
+            [$sample, $target, $maxIndex] = self::processLine($line, $maxIndex);
             $samples[] = $sample;
             $targets[] = $target;
         }

src/FeatureSelection/ScoringFunction/UnivariateLinearRegression.php

@@ -46,7 +46,7 @@ final class UnivariateLinearRegression implements ScoringFunction
         foreach (array_keys($samples[0]) as $index) {
             $featureColumn = array_column($samples, $index);
             $correlations[$index] =
-                (Matrix::dot($targets, $featureColumn)[0] / (new Matrix($featureColumn, false))->transpose()->frobeniusNorm())
+                Matrix::dot($targets, $featureColumn)[0] / (new Matrix($featureColumn, false))->transpose()->frobeniusNorm()
                 / (new Matrix($targets, false))->frobeniusNorm();
         }
 

src/Math/Kernel/RBF.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\Math\Kernel;
 
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\Math\Kernel;
 use Phpml\Math\Product;
 
@@ -19,12 +20,12 @@ class RBF implements Kernel
         $this->gamma = $gamma;
     }
 
-    /**
-     * @param array $a
-     * @param array $b
-     */
     public function compute($a, $b): float
     {
+        if (!is_array($a) || !is_array($b)) {
+            throw new InvalidArgumentException(sprintf('Arguments of %s must be arrays', __METHOD__));
+        }
+
         $score = 2 * Product::scalar($a, $b);
         $squares = Product::scalar($a, $a) + Product::scalar($b, $b);
 

src/Math/LinearAlgebra/EigenvalueDecomposition.php

@@ -502,7 +502,8 @@ class EigenvalueDecomposition
                     }
 
                     // Double division avoids possible underflow
-                    $g = ($g / $this->ort[$m]) / $this->H[$m][$m - 1];
+                    $g /= $this->ort[$m];
+                    $g /= $this->H[$m][$m - 1];
                     for ($i = $m; $i <= $high; ++$i) {
                         $this->V[$i][$j] += $g * $this->ort[$i];
                     }
@@ -734,7 +735,7 @@ class EigenvalueDecomposition
 
                 // Double QR step involving rows l:n and columns m:n
                 for ($k = $m; $k <= $n - 1; ++$k) {
-                    $notlast = ($k != $n - 1);
+                    $notlast = $k != $n - 1;
                     if ($k != $m) {
                         $p = $this->H[$k][$k - 1];
                         $q = $this->H[$k + 1][$k - 1];

src/NeuralNetwork/Network/MultilayerPerceptron.php

@@ -59,8 +59,14 @@ abstract class MultilayerPerceptron extends LayeredNetwork implements Estimator,
     /**
      * @throws InvalidArgumentException
      */
-    public function __construct(int $inputLayerFeatures, array $hiddenLayers, array $classes, int $iterations = 10000, ?ActivationFunction $activationFunction = null, float $learningRate = 1)
+    public function __construct(
-    {
+        int $inputLayerFeatures,
+        array $hiddenLayers,
+        array $classes,
+        int $iterations = 10000,
+        ?ActivationFunction $activationFunction = null,
+        float $learningRate = 1.
+    ) {
         if (count($hiddenLayers) === 0) {
             throw new InvalidArgumentException('Provide at least 1 hidden layer');
         }

tests/Helper/Optimizer/ConjugateGradientTest.php

@@ -23,7 +23,7 @@ class ConjugateGradientTest extends TestCase
 
         $callback = function ($theta, $sample, $target) {
             $y = $theta[0] + $theta[1] * $sample[0];
-            $cost = ($y - $target) ** 2 / 2;
+            $cost = (($y - $target) ** 2) / 2;
             $grad = $y - $target;
 
             return [$cost, $grad];
@@ -49,7 +49,7 @@ class ConjugateGradientTest extends TestCase
 
         $callback = function ($theta, $sample, $target) {
             $y = $theta[0] + $theta[1] * $sample[0];
-            $cost = ($y - $target) ** 2 / 2;
+            $cost = (($y - $target) ** 2) / 2;
             $grad = $y - $target;
 
             return [$cost, $grad];
@@ -78,7 +78,7 @@ class ConjugateGradientTest extends TestCase
 
         $callback = function ($theta, $sample, $target) {
             $y = $theta[0] + $theta[1] * $sample[0] + $theta[2] * $sample[1];
-            $cost = ($y - $target) ** 2 / 2;
+            $cost = (($y - $target) ** 2) / 2;
             $grad = $y - $target;
 
             return [$cost, $grad];

tests/Helper/Optimizer/GDTest.php

@@ -22,7 +22,7 @@ class GDTest extends TestCase
 
         $callback = function ($theta, $sample, $target) {
             $y = $theta[0] + $theta[1] * $sample[0];
-            $cost = ($y - $target) ** 2 / 2;
+            $cost = (($y - $target) ** 2) / 2;
             $grad = $y - $target;
 
             return [$cost, $grad];
@@ -49,7 +49,7 @@ class GDTest extends TestCase
 
         $callback = function ($theta, $sample, $target) {
             $y = $theta[0] + $theta[1] * $sample[0] + $theta[2] * $sample[1];
-            $cost = ($y - $target) ** 2 / 2;
+            $cost = (($y - $target) ** 2) / 2;
             $grad = $y - $target;
 
             return [$cost, $grad];

tests/Helper/Optimizer/StochasticGDTest.php

@@ -22,7 +22,7 @@ class StochasticGDTest extends TestCase
 
         $callback = function ($theta, $sample, $target) {
             $y = $theta[0] + $theta[1] * $sample[0];
-            $cost = ($y - $target) ** 2 / 2;
+            $cost = (($y - $target) ** 2) / 2;
             $grad = $y - $target;
 
             return [$cost, $grad];
@@ -49,7 +49,7 @@ class StochasticGDTest extends TestCase
 
         $callback = function ($theta, $sample, $target) {
             $y = $theta[0] + $theta[1] * $sample[0] + $theta[2] * $sample[1];
-            $cost = ($y - $target) ** 2 / 2;
+            $cost = (($y - $target) ** 2) / 2;
             $grad = $y - $target;
 
             return [$cost, $grad];

tests/Math/Kernel/RBFTest.php

@@ -4,6 +4,7 @@ declare(strict_types=1);
 
 namespace Phpml\Tests\Math\Kernel;
 
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\Math\Kernel\RBF;
 use PHPUnit\Framework\TestCase;
 
@@ -23,4 +24,12 @@ class RBFTest extends TestCase
         self::assertEquals(0.00451, $rbf->compute([1, 2, 3], [4, 5, 6]), '', $delta = 0.0001);
         self::assertEquals(0, $rbf->compute([4, 5], [1, 100]));
     }
+
+    public function testThrowExceptionWhenComputeArgumentIsNotAnArray(): void
+    {
+        $this->expectException(InvalidArgumentException::class);
+        $this->expectExceptionMessage('Arguments of Phpml\\Math\\Kernel\\RBF::compute must be arrays');
+
+        (new RBF(0.1))->compute([0], 1.0);
+    }
 }