mirror of
https://github.com/Llewellynvdm/php-ml.git
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130 lines
4.7 KiB
PHP
130 lines
4.7 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\Classification\Ensemble;
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use Phpml\Classification\DecisionTree;
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use Phpml\Classification\Ensemble\Bagging;
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use Phpml\Classification\NaiveBayes;
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use Phpml\ModelManager;
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use PHPUnit\Framework\TestCase;
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class BaggingTest extends TestCase
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{
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private $data = [
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['sunny', 85, 85, 'false', 'Dont_play'],
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['sunny', 80, 90, 'true', 'Dont_play'],
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['overcast', 83, 78, 'false', 'Play'],
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['rain', 70, 96, 'false', 'Play'],
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['rain', 68, 80, 'false', 'Play'],
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['rain', 65, 70, 'true', 'Dont_play'],
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['overcast', 64, 65, 'true', 'Play'],
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['sunny', 72, 95, 'false', 'Dont_play'],
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['sunny', 69, 70, 'false', 'Play'],
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['rain', 75, 80, 'false', 'Play'],
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['sunny', 75, 70, 'true', 'Play'],
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['overcast', 72, 90, 'true', 'Play'],
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['overcast', 81, 75, 'false', 'Play'],
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['rain', 71, 80, 'true', 'Dont_play'],
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];
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private $extraData = [
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['scorching', 90, 95, 'false', 'Dont_play'],
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['scorching', 0, 0, 'false', 'Dont_play'],
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];
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public function testPredictSingleSample()
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{
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[$data, $targets] = $this->getData($this->data);
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$classifier = $this->getClassifier();
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// Testing with default options
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$classifier->train($data, $targets);
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$this->assertEquals('Dont_play', $classifier->predict(['sunny', 78, 72, 'false']));
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$this->assertEquals('Play', $classifier->predict(['overcast', 60, 60, 'false']));
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$this->assertEquals('Dont_play', $classifier->predict(['rain', 60, 60, 'true']));
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[$data, $targets] = $this->getData($this->extraData);
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$classifier->train($data, $targets);
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$this->assertEquals('Dont_play', $classifier->predict(['scorching', 95, 90, 'true']));
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$this->assertEquals('Play', $classifier->predict(['overcast', 60, 60, 'false']));
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return $classifier;
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}
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public function testSaveAndRestore(): void
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{
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[$data, $targets] = $this->getData($this->data);
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$classifier = $this->getClassifier(5);
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$classifier->train($data, $targets);
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$testSamples = [['sunny', 78, 72, 'false'], ['overcast', 60, 60, 'false']];
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$predicted = $classifier->predict($testSamples);
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$filename = 'bagging-test-'.random_int(100, 999).'-'.uniqid();
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$filepath = tempnam(sys_get_temp_dir(), $filename);
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$modelManager = new ModelManager();
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$modelManager->saveToFile($classifier, $filepath);
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$restoredClassifier = $modelManager->restoreFromFile($filepath);
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$this->assertEquals($classifier, $restoredClassifier);
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$this->assertEquals($predicted, $restoredClassifier->predict($testSamples));
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}
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public function testBaseClassifiers(): void
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{
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[$data, $targets] = $this->getData($this->data);
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$baseClassifiers = $this->getAvailableBaseClassifiers();
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foreach ($baseClassifiers as $base => $params) {
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$classifier = $this->getClassifier();
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$classifier->setClassifer($base, $params);
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$classifier->train($data, $targets);
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$baseClassifier = new $base(...array_values($params));
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$baseClassifier->train($data, $targets);
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$testData = [['sunny', 78, 72, 'false'], ['overcast', 60, 60, 'false'], ['rain', 60, 60, 'true']];
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foreach ($testData as $test) {
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$result = $classifier->predict($test);
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$baseResult = $classifier->predict($test);
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$this->assertEquals($result, $baseResult);
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}
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}
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}
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protected function getClassifier($numBaseClassifiers = 50)
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{
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$classifier = new Bagging($numBaseClassifiers);
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$classifier->setSubsetRatio(1.0);
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$classifier->setClassifer(DecisionTree::class, ['depth' => 10]);
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return $classifier;
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}
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protected function getAvailableBaseClassifiers()
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{
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return [
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DecisionTree::class => ['depth' => 5],
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NaiveBayes::class => [],
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];
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}
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private function getData($input)
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{
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// Populating input data to a size large enough
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// for base classifiers that they can work with a subset of it
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$populated = [];
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for ($i = 0; $i < 20; ++$i) {
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$populated = array_merge($populated, $input);
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}
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shuffle($populated);
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$targets = array_column($populated, 4);
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array_walk($populated, function (&$v): void {
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array_splice($v, 4, 1);
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});
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return [$populated, $targets];
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}
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}
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