php-ml/tests/Classification/Linear/DecisionStumpTest.php
2018-02-10 12:08:58 +01:00

76 lines
3.0 KiB
PHP

<?php
declare(strict_types=1);
namespace Phpml\Tests\Classification\Linear;
use Phpml\Classification\Linear\DecisionStump;
use Phpml\ModelManager;
use PHPUnit\Framework\TestCase;
class DecisionStumpTest extends TestCase
{
public function testPredictSingleSample()
{
// Samples should be separable with a line perpendicular
// to any dimension given in the dataset
//
// First: horizontal test
$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
$targets = [0, 0, 1, 1];
$classifier = new DecisionStump();
$classifier->train($samples, $targets);
$this->assertEquals(0, $classifier->predict([0.1, 0.2]));
$this->assertEquals(0, $classifier->predict([1.1, 0.2]));
$this->assertEquals(1, $classifier->predict([0.1, 0.99]));
$this->assertEquals(1, $classifier->predict([1.1, 0.8]));
// Then: vertical test
$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
$targets = [0, 1, 0, 1];
$classifier = new DecisionStump();
$classifier->train($samples, $targets);
$this->assertEquals(0, $classifier->predict([0.1, 0.2]));
$this->assertEquals(0, $classifier->predict([0.1, 1.1]));
$this->assertEquals(1, $classifier->predict([1.0, 0.99]));
$this->assertEquals(1, $classifier->predict([1.1, 0.1]));
// By use of One-v-Rest, DecisionStump can perform multi-class classification
// The samples should be separable by lines perpendicular to the dimensions
$samples = [
[0, 0], [0, 1], [1, 0], [1, 1], // First group : a cluster at bottom-left corner in 2D
[5, 5], [6, 5], [5, 6], [7, 5], // Second group: another cluster at the middle-right
[3, 10], [3, 10], [3, 8], [3, 9], // Third group : cluster at the top-middle
];
$targets = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2];
$classifier = new DecisionStump();
$classifier->train($samples, $targets);
$this->assertEquals(0, $classifier->predict([0.5, 0.5]));
$this->assertEquals(1, $classifier->predict([6.0, 5.0]));
$this->assertEquals(2, $classifier->predict([3.5, 9.5]));
return $classifier;
}
public function testSaveAndRestore(): void
{
// Instantinate new Percetron trained for OR problem
$samples = [[0, 0], [1, 0], [0, 1], [1, 1]];
$targets = [0, 1, 1, 1];
$classifier = new DecisionStump();
$classifier->train($samples, $targets);
$testSamples = [[0, 1], [1, 1], [0.2, 0.1]];
$predicted = $classifier->predict($testSamples);
$filename = 'dstump-test-'.random_int(100, 999).'-'.uniqid();
$filepath = tempnam(sys_get_temp_dir(), $filename);
$modelManager = new ModelManager();
$modelManager->saveToFile($classifier, $filepath);
$restoredClassifier = $modelManager->restoreFromFile($filepath);
$this->assertEquals($classifier, $restoredClassifier);
$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
}
}