php-ml/tests/Metric/AccuracyTest.php

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<?php
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declare(strict_types=1);
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namespace Phpml\Tests\Metric;
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use Phpml\Classification\SVC;
use Phpml\CrossValidation\RandomSplit;
use Phpml\Dataset\Demo\IrisDataset;
use Phpml\Exception\InvalidArgumentException;
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use Phpml\Metric\Accuracy;
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use Phpml\SupportVectorMachine\Kernel;
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use PHPUnit\Framework\TestCase;
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class AccuracyTest extends TestCase
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{
public function testThrowExceptionOnInvalidArguments(): void
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{
$this->expectException(InvalidArgumentException::class);
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$actualLabels = ['a', 'b', 'a', 'b'];
$predictedLabels = ['a', 'a'];
Accuracy::score($actualLabels, $predictedLabels);
}
public function testCalculateNormalizedScore(): void
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{
$actualLabels = ['a', 'b', 'a', 'b'];
$predictedLabels = ['a', 'a', 'b', 'b'];
$this->assertEquals(0.5, Accuracy::score($actualLabels, $predictedLabels));
}
public function testCalculateNotNormalizedScore(): void
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{
$actualLabels = ['a', 'b', 'a', 'b'];
$predictedLabels = ['a', 'b', 'b', 'b'];
$this->assertEquals(3, Accuracy::score($actualLabels, $predictedLabels, false));
}
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public function testAccuracyOnDemoDataset(): void
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{
$dataset = new RandomSplit(new IrisDataset(), 0.5, 123);
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$classifier = new SVC(Kernel::RBF);
$classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
$predicted = (array) $classifier->predict($dataset->getTestSamples());
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$accuracy = Accuracy::score($dataset->getTestLabels(), $predicted);
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$expected = PHP_VERSION_ID >= 70100 ? 1 : 0.959;
$this->assertEquals($expected, $accuracy, '', 0.01);
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}
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}