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