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integration tests for knn classifier

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This commit is contained in:
Arkadiusz Kondas 2016-04-08 22:49:17 +02:00
parent e7d2780150
commit 62ec4ec2f2
4 changed files with 25 additions and 12 deletions
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2
src/Phpml/Classifier/KNearestNeighbors.php
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@ -50,7 +50,7 @@ class KNearestNeighbors implements Classifier
*/ */
public function predict(array $samples) public function predict(array $samples)
{ {
if(!is_array($samples[0])) { if (!is_array($samples[0])) {
$predicted = $this->predictSample($samples); $predicted = $this->predictSample($samples);
} else { } else {
$predicted = []; $predicted = [];

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

18
tests/Phpml/Classifier/KNearestNeighborsTest.php
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@ -5,10 +5,13 @@ declare (strict_types = 1);
namespace tests\Classifier; namespace tests\Classifier;
use Phpml\Classifier\KNearestNeighbors; use Phpml\Classifier\KNearestNeighbors;
use Phpml\CrossValidation\RandomSplit;
use Phpml\Dataset\Demo\Iris;
use Phpml\Metric\Accuracy;
class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase 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]]; $samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$labels = ['a', 'a', 'a', 'b', 'b', 'b']; $labels = ['a', 'a', 'a', 'b', 'b', 'b'];
@ -33,7 +36,7 @@ class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
$trainLabels = ['a', 'a', 'a', 'b', 'b', 'b']; $trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
$testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]]; $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 = new KNearestNeighbors();
$classifier->train($trainSamples, $trainLabels); $classifier->train($trainSamples, $trainLabels);
@ -41,4 +44,15 @@ class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
$this->assertEquals($testLabels, $predicted); $this->assertEquals($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);
}
} }

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