ML-385 code refactoring, added ValidatesShapes

Author: apphp

src/NeuralNet/CostFunctions/CrossEntropy/CrossEntropy.php

@@ -4,11 +4,10 @@
 
 namespace Rubix\ML\NeuralNet\CostFunctions\CrossEntropy;
 
-use NumPower;
 use NDArray;
-use Rubix\ML\Exceptions\InvalidArgumentException;
+use NumPower;
 use Rubix\ML\NeuralNet\CostFunctions\Base\Contracts\ClassificationLoss;
-
+use Rubix\ML\Traits\ValidatesShapes;
 use const Rubix\ML\EPSILON;
 
 /**
@@ -28,6 +27,8 @@
  */
 class CrossEntropy implements ClassificationLoss
 {
+    use ValidatesShapes;
+
     /**
      * Compute the loss score.
      *
@@ -39,9 +40,7 @@ class CrossEntropy implements ClassificationLoss
      */
     public function compute(NDArray $output, NDArray $target) : float
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         // Clip values to avoid log(0)
         $output = NumPower::clip($output, EPSILON, 1.0);
@@ -64,9 +63,7 @@ public function compute(NDArray $output, NDArray $target) : float
      */
     public function differentiate(NDArray $output, NDArray $target) : NDArray
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         // Numerator = ŷ - y (calculate before clipping to preserve zeros)
         $numerator = NumPower::subtract($output, $target);

src/NeuralNet/CostFunctions/HuberLoss/HuberLoss.php

@@ -7,8 +7,8 @@
 use NDArray;
 use NumPower;
 use Rubix\ML\NeuralNet\CostFunctions\Base\Contracts\RegressionLoss;
-use Rubix\ML\Exceptions\InvalidArgumentException;
 use Rubix\ML\NeuralNet\CostFunctions\HuberLoss\Exceptions\InvalidAlphaException;
+use Rubix\ML\Traits\ValidatesShapes;
 
 /**
  * Huber Loss
@@ -25,6 +25,8 @@
  */
 class HuberLoss implements RegressionLoss
 {
+    use ValidatesShapes;
+
     /**
      * The alpha quantile i.e the pivot point at which numbers larger will be
      * evalutated with an L1 loss while number smaller will be evalutated with
@@ -68,9 +70,7 @@ public function __construct(float $alpha = 0.9)
      */
     public function compute(NDArray $output, NDArray $target) : float
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         $difference = NumPower::subtract($target, $output);
         $scaled = NumPower::divide($difference, $this->alpha);
@@ -94,9 +94,7 @@ public function compute(NDArray $output, NDArray $target) : float
      */
     public function differentiate(NDArray $output, NDArray $target) : NDArray
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         $difference = NumPower::subtract($output, $target);
         $squared = NumPower::pow($difference, 2);

src/NeuralNet/CostFunctions/LeastSquares/LeastSquares.php

@@ -4,10 +4,10 @@
 
 namespace Rubix\ML\NeuralNet\CostFunctions\LeastSquares;
 
-use InvalidArgumentException;
 use NDArray;
 use NumPower;
 use Rubix\ML\NeuralNet\CostFunctions\Base\Contracts\RegressionLoss;
+use Rubix\ML\Traits\ValidatesShapes;
 
 /**
  * Least Squares
@@ -22,6 +22,8 @@
  */
 class LeastSquares implements RegressionLoss
 {
+    use ValidatesShapes;
+
     /**
      * Compute the loss score.
      *
@@ -33,9 +35,7 @@ class LeastSquares implements RegressionLoss
      */
     public function compute(NDArray $output, NDArray $target) : float
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         $difference = NumPower::subtract($output, $target);
         $squared = NumPower::pow($difference, 2);
@@ -55,9 +55,7 @@ public function compute(NDArray $output, NDArray $target) : float
      */
     public function differentiate(NDArray $output, NDArray $target) : NDArray
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         return NumPower::subtract($output, $target);
     }

src/NeuralNet/CostFunctions/MeanAbsoluteError/MeanAbsoluteError.php

@@ -4,10 +4,10 @@
 
 namespace Rubix\ML\NeuralNet\CostFunctions\MeanAbsoluteError;
 
-use InvalidArgumentException;
 use NDArray;
 use NumPower;
 use Rubix\ML\NeuralNet\CostFunctions\Base\Contracts\RegressionLoss;
+use Rubix\ML\Traits\ValidatesShapes;
 
 /**
  * Mean Absolute Error
@@ -23,6 +23,8 @@
  */
 class MeanAbsoluteError implements RegressionLoss
 {
+    use ValidatesShapes;
+
     /**
      * Compute the loss score.
      *
@@ -34,9 +36,7 @@ class MeanAbsoluteError implements RegressionLoss
      */
     public function compute(NDArray $output, NDArray $target) : float
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         $difference = NumPower::subtract($output, $target);
         $absolute = NumPower::abs($difference);
@@ -55,9 +55,7 @@ public function compute(NDArray $output, NDArray $target) : float
      */
     public function differentiate(NDArray $output, NDArray $target) : NDArray
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         $difference = NumPower::subtract($output, $target);
 

src/NeuralNet/CostFunctions/RelativeEntropy/RelativeEntropy.php

@@ -4,11 +4,10 @@
 
 namespace Rubix\ML\NeuralNet\CostFunctions\RelativeEntropy;
 
-use NumPower;
 use NDArray;
-use Rubix\ML\Exceptions\InvalidArgumentException;
+use NumPower;
 use Rubix\ML\NeuralNet\CostFunctions\Base\Contracts\ClassificationLoss;
-
+use Rubix\ML\Traits\ValidatesShapes;
 use const Rubix\ML\EPSILON;
 
 /**
@@ -24,6 +23,8 @@
  */
 class RelativeEntropy implements ClassificationLoss
 {
+    use ValidatesShapes;
+
     /**
      * Compute the loss.
      *
@@ -37,9 +38,7 @@ class RelativeEntropy implements ClassificationLoss
      */
     public function compute(NDArray $output, NDArray $target) : float
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         // Clip values to avoid log(0)
         $target = NumPower::clip($target, EPSILON, 1.0);
@@ -65,9 +64,7 @@ public function compute(NDArray $output, NDArray $target) : float
      */
     public function differentiate(NDArray $output, NDArray $target) : NDArray
     {
-        if ($output->shape() !== $target->shape()) {
-            throw new InvalidArgumentException('Output and target must have the same shape.');
-        }
+        $this->validateShapes($output, $target);
 
         // Clip values to avoid division by zero
         $target = NumPower::clip($target, EPSILON, 1.0);

src/Traits/ValidatesShapes.php

@@ -0,0 +1,35 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Rubix\ML\Traits;
+
+use InvalidArgumentException;
+use NDArray;
+
+/**
+ * Validates Shapes
+ *
+ * A trait that provides shape validation for cost functions to ensure
+ * output and target arrays have matching dimensions.
+ *
+ * @category    Machine Learning
+ * @package     Rubix/ML
+ * @author      Samuel Akopyan <[email protected]>
+ */
+trait ValidatesShapes
+{
+    /**
+     * Validate that output and target have the same shape.
+     *
+     * @param NDArray $output
+     * @param NDArray $target
+     * @throws InvalidArgumentException
+     */
+    protected function validateShapes(NDArray $output, NDArray $target) : void
+    {
+        if ($output->shape() !== $target->shape()) {
+            throw new InvalidArgumentException('Output and target must have the same shape.');
+        }
+    }
+}