56 lines
1.5 KiB
PHP
56 lines
1.5 KiB
PHP
<?php
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declare (strict_types = 1);
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namespace tests\Phpml\Metric;
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use Phpml\Classification\SVC;
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use Phpml\CrossValidation\RandomSplit;
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use Phpml\Dataset\Demo\IrisDataset;
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use Phpml\Metric\Accuracy;
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use Phpml\SupportVectorMachine\Kernel;
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class AccuracyTest extends \PHPUnit_Framework_TestCase
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{
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/**
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* @expectedException \Phpml\Exception\InvalidArgumentException
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*/
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public function testThrowExceptionOnInvalidArguments()
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{
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$actualLabels = ['a', 'b', 'a', 'b'];
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$predictedLabels = ['a', 'a'];
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Accuracy::score($actualLabels, $predictedLabels);
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}
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public function testCalculateNormalizedScore()
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{
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$actualLabels = ['a', 'b', 'a', 'b'];
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$predictedLabels = ['a', 'a', 'b', 'b'];
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$this->assertEquals(0.5, Accuracy::score($actualLabels, $predictedLabels));
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}
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public function testCalculateNotNormalizedScore()
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{
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$actualLabels = ['a', 'b', 'a', 'b'];
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$predictedLabels = ['a', 'b', 'b', 'b'];
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$this->assertEquals(3, Accuracy::score($actualLabels, $predictedLabels, false));
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}
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public function testAccuracyOnDemoDataset()
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{
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$dataset = new RandomSplit(new IrisDataset(), 0.5, 123);
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$classifier = new SVC(Kernel::RBF);
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$classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
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$predicted = $classifier->predict($dataset->getTestSamples());
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$accuracy = Accuracy::score($dataset->getTestLabels(), $predicted);
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$this->assertEquals(0.959, $accuracy, '', 0.01);
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}
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}
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