integration tests for knn classifier

src/Phpml/Classifier/KNearestNeighbors.php

@@ -50,7 +50,7 @@ class KNearestNeighbors implements Classifier
      */
     public function predict(array $samples)
     {
-        if(!is_array($samples[0])) {
+        if (!is_array($samples[0])) {
             $predicted = $this->predictSample($samples);
         } else {
             $predicted = [];

src/Phpml/Metric/Accuracy.php

@@ -1,5 +1,6 @@
 <?php
-declare(strict_types = 1);
+
+declare (strict_types = 1);
 
 namespace Phpml\Metric;
 
@@ -7,11 +8,10 @@ use Phpml\Exception\InvalidArgumentException;
 
 class Accuracy
 {
-
     /**
      * @param array $actualLabels
      * @param array $predictedLabels
-     * @param bool $normalize
+     * @param bool  $normalize
      *
      * @return float|int
      *
@@ -25,12 +25,12 @@ class Accuracy
 
         $score = 0;
         foreach ($actualLabels as $index => $label) {
-            if($label===$predictedLabels[$index]) {
-                $score++;
+            if ($label === $predictedLabels[$index]) {
+                ++$score;
             }
         }
 
-        if($normalize) {
+        if ($normalize) {
             $score = $score / count($actualLabels);
         }
 

tests/Phpml/Classifier/KNearestNeighborsTest.php

@@ -5,10 +5,13 @@ declare (strict_types = 1);
 namespace tests\Classifier;
 
 use Phpml\Classifier\KNearestNeighbors;
+use Phpml\CrossValidation\RandomSplit;
+use Phpml\Dataset\Demo\Iris;
+use Phpml\Metric\Accuracy;
 
 class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
 {
-    public function testPredictSimpleSampleWithDefaultK()
+    public function testPredictSingleSampleWithDefaultK()
     {
         $samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
         $labels = ['a', 'a', 'a', 'b', 'b', 'b'];
@@ -33,7 +36,7 @@ class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
         $trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
 
         $testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]];
-        $testLabels = ['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a',];
+        $testLabels = ['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a'];
 
         $classifier = new KNearestNeighbors();
         $classifier->train($trainSamples, $trainLabels);
@@ -41,4 +44,15 @@ class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
 
         $this->assertEquals($testLabels, $predicted);
     }
+
+    public function testAccuracyOnIrisDataset()
+    {
+        $dataset = new RandomSplit(new Iris(), $testSize = 0.5, $seed = 123);
+        $classifier = new KNearestNeighbors($k = 4);
+        $classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
+        $predicted = $classifier->predict($dataset->getTestSamples());
+        $score = Accuracy::score($dataset->getTestLabels(), $predicted);
+
+        $this->assertEquals(0.96, $score);
+    }
 }

tests/Phpml/Metric/AccuracyTest.php

@@ -1,5 +1,6 @@
 <?php
-declare(strict_types = 1);
+
+declare (strict_types = 1);
 
 namespace tests\Phpml\Metric;
 
@@ -7,7 +8,6 @@ use Phpml\Metric\Accuracy;
 
 class AccuracyTest extends \PHPUnit_Framework_TestCase
 {
-
     /**
      * @expectedException \Phpml\Exception\InvalidArgumentException
      */
@@ -34,5 +34,4 @@ class AccuracyTest extends \PHPUnit_Framework_TestCase
 
         $this->assertEquals(3, Accuracy::score($actualLabels, $predictedLabels, false));
     }
-
 }