Throw proper exception (#259)

* Throw proper exception

* Fix coding style

src/Classification/Ensemble/AdaBoost.php

@@ -4,10 +4,10 @@
 
 namespace Phpml\Classification\Ensemble;
 
-use Exception;
 use Phpml\Classification\Classifier;
 use Phpml\Classification\Linear\DecisionStump;
 use Phpml\Classification\WeightedClassifier;
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\Helper\Predictable;
 use Phpml\Helper\Trainable;
 use Phpml\Math\Statistic\Mean;
@@ -93,14 +93,14 @@ public function setBaseClassifier(string $baseClassifier = DecisionStump::class,
     }
 
     /**
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     public function train(array $samples, array $targets): void
     {
         // Initialize usual variables
         $this->labels = array_keys(array_count_values($targets));
         if (count($this->labels) != 2) {
-            throw new Exception('AdaBoost is a binary classifier and can classify between two classes only');
+            throw new InvalidArgumentException('AdaBoost is a binary classifier and can classify between two classes only');
         }
 
         // Set all target values to either -1 or 1

src/Classification/Ensemble/Bagging.php

@@ -4,9 +4,9 @@
 
 namespace Phpml\Classification\Ensemble;
 
-use Exception;
 use Phpml\Classification\Classifier;
 use Phpml\Classification\DecisionTree;
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\Helper\Predictable;
 use Phpml\Helper\Trainable;
 use ReflectionClass;
@@ -77,12 +77,12 @@ public function __construct(int $numClassifier = 50)
      *
      * @return $this
      *
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     public function setSubsetRatio(float $ratio)
     {
         if ($ratio < 0.1 || $ratio > 1.0) {
-            throw new Exception('Subset ratio should be between 0.1 and 1.0');
+            throw new InvalidArgumentException('Subset ratio should be between 0.1 and 1.0');
         }
 
         $this->subsetRatio = $ratio;

src/Classification/Linear/Adaline.php

@@ -4,7 +4,7 @@
 
 namespace Phpml\Classification\Linear;
 
-use Exception;
+use Phpml\Exception\InvalidArgumentException;
 
 class Adaline extends Perceptron
 {
@@ -34,7 +34,7 @@ class Adaline extends Perceptron
      * If normalizeInputs is set to true, then every input given to the algorithm will be standardized
      * by use of standard deviation and mean calculation
      *
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     public function __construct(
         float $learningRate = 0.001,
@@ -43,7 +43,7 @@ public function __construct(
         int $trainingType = self::BATCH_TRAINING
     ) {
         if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING], true)) {
-            throw new Exception('Adaline can only be trained with batch and online/stochastic gradient descent algorithm');
+            throw new InvalidArgumentException('Adaline can only be trained with batch and online/stochastic gradient descent algorithm');
         }
 
         $this->trainingType = $trainingType;

src/Classification/Linear/DecisionStump.php

@@ -4,9 +4,9 @@
 
 namespace Phpml\Classification\Linear;
 
-use Exception;
 use Phpml\Classification\DecisionTree;
 use Phpml\Classification\WeightedClassifier;
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\Helper\OneVsRest;
 use Phpml\Helper\Predictable;
 use Phpml\Math\Comparison;
@@ -104,7 +104,7 @@ public function setNumericalSplitCount(float $count): void
     }
 
     /**
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     protected function trainBinary(array $samples, array $targets, array $labels): void
     {
@@ -121,7 +121,7 @@ protected function trainBinary(array $samples, array $targets, array $labels): v
         if (!empty($this->weights)) {
             $numWeights = count($this->weights);
             if ($numWeights != count($samples)) {
-                throw new Exception('Number of sample weights does not match with number of samples');
+                throw new InvalidArgumentException('Number of sample weights does not match with number of samples');
             }
         } else {
             $this->weights = array_fill(0, count($samples), 1);

src/Classification/Linear/LogisticRegression.php

@@ -6,6 +6,7 @@
 
 use Closure;
 use Exception;
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\Helper\Optimizer\ConjugateGradient;
 
 class LogisticRegression extends Adaline
@@ -61,7 +62,7 @@ class LogisticRegression extends Adaline
      *
      * Penalty (Regularization term) can be 'L2' or empty string to cancel penalty term
      *
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     public function __construct(
         int $maxIterations = 500,
@@ -72,18 +73,24 @@ public function __construct(
     ) {
         $trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
         if (!in_array($trainingType, $trainingTypes, true)) {
-            throw new Exception('Logistic regression can only be trained with '.
+            throw new InvalidArgumentException(
+                'Logistic regression can only be trained with '.
                 'batch (gradient descent), online (stochastic gradient descent) '.
-                'or conjugate batch (conjugate gradients) algorithms');
+                'or conjugate batch (conjugate gradients) algorithms'
+            );
         }
 
         if (!in_array($cost, ['log', 'sse'], true)) {
-            throw new Exception("Logistic regression cost function can be one of the following: \n".
-                "'log' for log-likelihood and 'sse' for sum of squared errors");
+            throw new InvalidArgumentException(
+                "Logistic regression cost function can be one of the following: \n".
+                "'log' for log-likelihood and 'sse' for sum of squared errors"
+            );
         }
 
         if ($penalty != '' && strtoupper($penalty) !== 'L2') {
-            throw new Exception("Logistic regression supports only 'L2' regularization");
+            throw new InvalidArgumentException(
+                "Logistic regression supports only 'L2' regularization"
+            );
         }
 
         $this->learningRate = 0.001;
@@ -140,7 +147,8 @@ protected function runTraining(array $samples, array $targets): void
                 return;
 
             default:
-                throw new Exception('Logistic regression has invalid training type: %s.', $this->trainingType);
+                // Not reached
+                throw new Exception(sprintf('Logistic regression has invalid training type: %d.', $this->trainingType));
         }
     }
 
@@ -232,6 +240,7 @@ protected function getCostFunction(): Closure
                 return $callback;
 
             default:
+                // Not reached
                 throw new Exception(sprintf('Logistic regression has invalid cost function: %s.', $this->costFunction));
         }
     }

src/Classification/Linear/Perceptron.php

@@ -5,8 +5,8 @@
 namespace Phpml\Classification\Linear;
 
 use Closure;
-use Exception;
 use Phpml\Classification\Classifier;
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\Helper\OneVsRest;
 use Phpml\Helper\Optimizer\GD;
 use Phpml\Helper\Optimizer\StochasticGD;
@@ -70,16 +70,16 @@ class Perceptron implements Classifier, IncrementalEstimator
      * @param float $learningRate  Value between 0.0(exclusive) and 1.0(inclusive)
      * @param int   $maxIterations Must be at least 1
      *
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true)
     {
         if ($learningRate <= 0.0 || $learningRate > 1.0) {
-            throw new Exception('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
+            throw new InvalidArgumentException('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
         }
 
         if ($maxIterations <= 0) {
-            throw new Exception('Maximum number of iterations must be an integer greater than 0');
+            throw new InvalidArgumentException('Maximum number of iterations must be an integer greater than 0');
         }
 
         if ($normalizeInputs) {

src/DimensionReduction/KernelPCA.php

@@ -6,6 +6,8 @@
 
 use Closure;
 use Exception;
+use Phpml\Exception\InvalidArgumentException;
+use Phpml\Exception\InvalidOperationException;
 use Phpml\Math\Distance\Euclidean;
 use Phpml\Math\Distance\Manhattan;
 use Phpml\Math\Matrix;
@@ -53,13 +55,13 @@ class KernelPCA extends PCA
      * @param int   $numFeatures   Number of columns to be returned
      * @param float $gamma         Gamma parameter is used with RBF and Sigmoid kernels
      *
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     public function __construct(int $kernel = self::KERNEL_RBF, ?float $totalVariance = null, ?int $numFeatures = null, ?float $gamma = null)
     {
         $availableKernels = [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN, self::KERNEL_LINEAR];
         if (!in_array($kernel, $availableKernels, true)) {
-            throw new Exception('KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian');
+            throw new InvalidArgumentException('KernelPCA can be initialized with the following kernels only: Linear, RBF, Sigmoid and Laplacian');
         }
 
         parent::__construct($totalVariance, $numFeatures);
@@ -97,16 +99,17 @@ public function fit(array $data): array
      * Transforms the given sample to a lower dimensional vector by using
      * the variables obtained during the last run of <code>fit</code>.
      *
-     * @throws \Exception
+     * @throws InvalidArgumentException
+     * @throws InvalidOperationException
      */
     public function transform(array $sample): array
     {
         if (!$this->fit) {
-            throw new Exception('KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first');
+            throw new InvalidOperationException('KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first');
         }
 
         if (is_array($sample[0])) {
-            throw new Exception('KernelPCA::transform() accepts only one-dimensional arrays');
+            throw new InvalidArgumentException('KernelPCA::transform() accepts only one-dimensional arrays');
         }
 
         $pairs = $this->getDistancePairs($sample);
@@ -199,6 +202,7 @@ protected function getKernel(): Closure
                 };
 
             default:
+                // Not reached
                 throw new Exception(sprintf('KernelPCA initialized with invalid kernel: %d', $this->kernel));
         }
     }

src/DimensionReduction/LDA.php

@@ -4,7 +4,8 @@
 
 namespace Phpml\DimensionReduction;
 
-use Exception;
+use Phpml\Exception\InvalidArgumentException;
+use Phpml\Exception\InvalidOperationException;
 use Phpml\Math\Matrix;
 
 class LDA extends EigenTransformerBase
@@ -46,20 +47,20 @@ class LDA extends EigenTransformerBase
      * @param float|null $totalVariance Total explained variance to be preserved
      * @param int|null   $numFeatures   Number of features to be preserved
      *
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     public function __construct(?float $totalVariance = null, ?int $numFeatures = null)
     {
         if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
-            throw new Exception('Total variance can be a value between 0.1 and 0.99');
+            throw new InvalidArgumentException('Total variance can be a value between 0.1 and 0.99');
         }
 
         if ($numFeatures !== null && $numFeatures <= 0) {
-            throw new Exception('Number of features to be preserved should be greater than 0');
+            throw new InvalidArgumentException('Number of features to be preserved should be greater than 0');
         }
 
-        if ($totalVariance !== null && $numFeatures !== null) {
-            throw new Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
+        if (($totalVariance !== null) === ($numFeatures !== null)) {
+            throw new InvalidArgumentException('Either totalVariance or numFeatures should be specified in order to run the algorithm');
         }
 
         if ($numFeatures !== null) {
@@ -94,12 +95,12 @@ public function fit(array $data, array $classes): array
      * Transforms the given sample to a lower dimensional vector by using
      * the eigenVectors obtained in the last run of <code>fit</code>.
      *
-     * @throws \Exception
+     * @throws InvalidOperationException
      */
     public function transform(array $sample): array
     {
         if (!$this->fit) {
-            throw new Exception('LDA has not been fitted with respect to original dataset, please run LDA::fit() first');
+            throw new InvalidOperationException('LDA has not been fitted with respect to original dataset, please run LDA::fit() first');
         }
 
         if (!is_array($sample[0])) {

src/DimensionReduction/PCA.php

@@ -4,7 +4,8 @@
 
 namespace Phpml\DimensionReduction;
 
-use Exception;
+use Phpml\Exception\InvalidArgumentException;
+use Phpml\Exception\InvalidOperationException;
 use Phpml\Math\Statistic\Covariance;
 use Phpml\Math\Statistic\Mean;
 
@@ -31,20 +32,20 @@ class PCA extends EigenTransformerBase
      * @param float $totalVariance Total explained variance to be preserved
      * @param int   $numFeatures   Number of features to be preserved
      *
-     * @throws \Exception
+     * @throws InvalidArgumentException
      */
     public function __construct(?float $totalVariance = null, ?int $numFeatures = null)
     {
         if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
-            throw new Exception('Total variance can be a value between 0.1 and 0.99');
+            throw new InvalidArgumentException('Total variance can be a value between 0.1 and 0.99');
         }
 
         if ($numFeatures !== null && $numFeatures <= 0) {
-            throw new Exception('Number of features to be preserved should be greater than 0');
+            throw new InvalidArgumentException('Number of features to be preserved should be greater than 0');
         }
 
-        if ($totalVariance !== null && $numFeatures !== null) {
-            throw new Exception('Either totalVariance or numFeatures should be specified in order to run the algorithm');
+        if (($totalVariance !== null) === ($numFeatures !== null)) {
+            throw new InvalidArgumentException('Either totalVariance or numFeatures should be specified in order to run the algorithm');
         }
 
         if ($numFeatures !== null) {
@@ -81,12 +82,12 @@ public function fit(array $data): array
      * Transforms the given sample to a lower dimensional vector by using
      * the eigenVectors obtained in the last run of <code>fit</code>.
      *
-     * @throws \Exception
+     * @throws InvalidOperationException
      */
     public function transform(array $sample): array
     {
         if (!$this->fit) {
-            throw new Exception('PCA has not been fitted with respect to original dataset, please run PCA::fit() first');
+            throw new InvalidOperationException('PCA has not been fitted with respect to original dataset, please run PCA::fit() first');
         }
 
         if (!is_array($sample[0])) {