php-ml/tests/Classification/Ensemble/RandomForestTest.php

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<?php
declare(strict_types=1);
namespace Phpml\Tests\Classification\Ensemble;
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use Phpml\Classification\DecisionTree;
use Phpml\Classification\Ensemble\RandomForest;
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use Phpml\Classification\NaiveBayes;
use Phpml\Exception\InvalidArgumentException;
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class RandomForestTest extends BaggingTest
{
public function testThrowExceptionWithInvalidClassifier(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('RandomForest can only use DecisionTree as base classifier');
$classifier = new RandomForest();
$classifier->setClassifer(NaiveBayes::class);
}
public function testThrowExceptionWithInvalidFeatureSubsetRatioType(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Feature subset ratio must be a string or a float');
$classifier = new RandomForest();
$classifier->setFeatureSubsetRatio(1);
}
public function testThrowExceptionWithInvalidFeatureSubsetRatioFloat(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('When a float is given, feature subset ratio should be between 0.1 and 1.0');
$classifier = new RandomForest();
$classifier->setFeatureSubsetRatio(1.1);
}
public function testThrowExceptionWithInvalidFeatureSubsetRatioString(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage("When a string is given, feature subset ratio can only be 'sqrt' or 'log'");
$classifier = new RandomForest();
$classifier->setFeatureSubsetRatio('pow');
}
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protected function getClassifier($numBaseClassifiers = 50)
{
$classifier = new RandomForest($numBaseClassifiers);
$classifier->setFeatureSubsetRatio('log');
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return $classifier;
}
protected function getAvailableBaseClassifiers()
{
return [DecisionTree::class => ['depth' => 5]];
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}
}