php-ml/tests/Classification/Linear/PerceptronTest.php
2018-10-28 07:44:52 +01:00

105 lines
4.5 KiB
PHP

<?php
declare(strict_types=1);
namespace Phpml\Tests\Classification\Linear;
use Phpml\Classification\Linear\Perceptron;
use Phpml\Exception\InvalidArgumentException;
use Phpml\ModelManager;
use PHPUnit\Framework\TestCase;
class PerceptronTest extends TestCase
{
public function testPerceptronThrowWhenLearningRateOutOfRange(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
new Perceptron(0, 5000);
}
public function testPerceptronThrowWhenMaxIterationsOutOfRange(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Maximum number of iterations must be an integer greater than 0');
new Perceptron(0.001, 0);
}
public function testPredictSingleSample(): void
{
// AND problem
$samples = [[0, 0], [1, 0], [0, 1], [1, 1], [0.6, 0.6]];
$targets = [0, 0, 0, 1, 1];
$classifier = new Perceptron(0.001, 5000);
$classifier->setEarlyStop(false);
$classifier->train($samples, $targets);
self::assertEquals(0, $classifier->predict([0.1, 0.2]));
self::assertEquals(0, $classifier->predict([0, 1]));
self::assertEquals(1, $classifier->predict([1.1, 0.8]));
// OR problem
$samples = [[0.1, 0.1], [0.4, 0.], [0., 0.3], [1, 0], [0, 1], [1, 1]];
$targets = [0, 0, 0, 1, 1, 1];
$classifier = new Perceptron(0.001, 5000, false);
$classifier->setEarlyStop(false);
$classifier->train($samples, $targets);
self::assertEquals(0, $classifier->predict([0., 0.]));
self::assertEquals(1, $classifier->predict([0.1, 0.99]));
self::assertEquals(1, $classifier->predict([1.1, 0.8]));
// By use of One-v-Rest, Perceptron 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 Perceptron();
$classifier->setEarlyStop(false);
$classifier->train($samples, $targets);
self::assertEquals(0, $classifier->predict([0.5, 0.5]));
self::assertEquals(1, $classifier->predict([6.0, 5.0]));
self::assertEquals(2, $classifier->predict([3.0, 9.5]));
// Extra partial training should lead to the same results.
$classifier->partialTrain([[0, 1], [1, 0]], [0, 0], [0, 1, 2]);
self::assertEquals(0, $classifier->predict([0.5, 0.5]));
self::assertEquals(1, $classifier->predict([6.0, 5.0]));
self::assertEquals(2, $classifier->predict([3.0, 9.5]));
// Train should clear previous data.
$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 = [2, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1];
$classifier->train($samples, $targets);
self::assertEquals(2, $classifier->predict([0.5, 0.5]));
self::assertEquals(0, $classifier->predict([6.0, 5.0]));
self::assertEquals(1, $classifier->predict([3.0, 9.5]));
}
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 Perceptron();
$classifier->train($samples, $targets);
$testSamples = [[0, 1], [1, 1], [0.2, 0.1]];
$predicted = $classifier->predict($testSamples);
$filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid('', false);
$filepath = (string) tempnam(sys_get_temp_dir(), $filename);
$modelManager = new ModelManager();
$modelManager->saveToFile($classifier, $filepath);
$restoredClassifier = $modelManager->restoreFromFile($filepath);
self::assertEquals($classifier, $restoredClassifier);
self::assertEquals($predicted, $restoredClassifier->predict($testSamples));
}
}