84 lines
3.0 KiB
PHP
84 lines
3.0 KiB
PHP
<?php
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declare (strict_types = 1);
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namespace tests\Classifier;
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use Phpml\Classifier\KNearestNeighbors;
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use Phpml\CrossValidation\RandomSplit;
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use Phpml\Dataset\Demo\Glass;
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use Phpml\Dataset\Demo\Iris;
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use Phpml\Dataset\Demo\Wine;
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use Phpml\Metric\Accuracy;
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class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
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{
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public function testPredictSingleSampleWithDefaultK()
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{
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$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$classifier = new KNearestNeighbors();
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$classifier->train($samples, $labels);
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$this->assertEquals('b', $classifier->predict([3, 2]));
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$this->assertEquals('b', $classifier->predict([5, 1]));
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$this->assertEquals('b', $classifier->predict([4, 3]));
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$this->assertEquals('b', $classifier->predict([4, -5]));
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$this->assertEquals('a', $classifier->predict([2, 3]));
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$this->assertEquals('a', $classifier->predict([1, 2]));
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$this->assertEquals('a', $classifier->predict([1, 5]));
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$this->assertEquals('a', $classifier->predict([3, 10]));
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}
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public function testPredictArrayOfSamples()
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{
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$trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]];
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$testLabels = ['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a'];
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$classifier = new KNearestNeighbors();
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$classifier->train($trainSamples, $trainLabels);
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$predicted = $classifier->predict($testSamples);
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$this->assertEquals($testLabels, $predicted);
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}
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public function testAccuracyOnIrisDataset()
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{
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$dataset = new RandomSplit(new Iris(), $testSize = 0.5, $seed = 123);
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$classifier = new KNearestNeighbors($k = 4);
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$classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
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$predicted = $classifier->predict($dataset->getTestSamples());
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$score = Accuracy::score($dataset->getTestLabels(), $predicted);
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$this->assertEquals(0.96, $score);
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}
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public function testAccuracyOnWineDataset()
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{
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$dataset = new RandomSplit(new Wine(), $testSize = 0.3, $seed = 321);
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$classifier = new KNearestNeighbors(1);
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$classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
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$predicted = $classifier->predict($dataset->getTestSamples());
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$score = Accuracy::score($dataset->getTestLabels(), $predicted);
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$this->assertEquals(0.85185185185185186, $score);
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}
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public function testAccuracyOnGlassDataset()
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{
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$dataset = new RandomSplit(new Glass(), $testSize = 0.3, $seed = 456);
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$classifier = new KNearestNeighbors(7);
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$classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
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$predicted = $classifier->predict($dataset->getTestSamples());
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$score = Accuracy::score($dataset->getTestLabels(), $predicted);
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$this->assertEquals(0.69230769230769229, $score);
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}
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}
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