110 lines
3.7 KiB
PHP
110 lines
3.7 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Tests\DimensionReduction;
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use Phpml\Dataset\Demo\IrisDataset;
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use Phpml\DimensionReduction\LDA;
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use Phpml\Exception\InvalidArgumentException;
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use Phpml\Exception\InvalidOperationException;
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use PHPUnit\Framework\TestCase;
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class LDATest extends TestCase
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{
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public function testLDA(): void
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{
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// Acceptable error
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$epsilon = 0.001;
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// IRIS dataset will be used to train LDA
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$dataset = new IrisDataset();
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$lda = new LDA(null, 2);
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$transformed = $lda->fit($dataset->getSamples(), $dataset->getTargets());
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// Some samples of the Iris data will be checked manually
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// First 3 and last 3 rows from the original dataset
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$data = [
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[5.1, 3.5, 1.4, 0.2],
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[4.9, 3.0, 1.4, 0.2],
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[4.7, 3.2, 1.3, 0.2],
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[6.5, 3.0, 5.2, 2.0],
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[6.2, 3.4, 5.4, 2.3],
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[5.9, 3.0, 5.1, 1.8],
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];
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$transformed2 = [
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[-1.4922092756753, 1.9047102045574],
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[-1.2576556684358, 1.608414450935],
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[-1.3487505965419, 1.749846351699],
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[1.7759343101456, 2.0371552314006],
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[2.0059819019159, 2.4493123003226],
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[1.701474913008, 1.9037880473772],
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];
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$control = [];
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$control = array_merge($control, array_slice($transformed, 0, 3));
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$control = array_merge($control, array_slice($transformed, -3));
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$check = function ($row1, $row2) use ($epsilon): void {
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// Due to the fact that the sign of values can be flipped
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// during the calculation of eigenValues, we have to compare
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// absolute value of the values
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$row1 = array_map('abs', $row1);
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$row2 = array_map('abs', $row2);
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self::assertEqualsWithDelta($row1, $row2, $epsilon);
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};
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array_map($check, $control, $transformed2);
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// Fitted LDA object should be able to return same values again
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// for each projected row
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foreach ($data as $i => $row) {
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$newRow = [$transformed2[$i]];
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$newRow2 = $lda->transform($row);
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array_map($check, $newRow, $newRow2);
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}
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}
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public function testLDAThrowWhenTotalVarianceOutOfRange(): void
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{
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$this->expectException(InvalidArgumentException::class);
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$this->expectExceptionMessage('Total variance can be a value between 0.1 and 0.99');
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new LDA(0., null);
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}
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public function testLDAThrowWhenNumFeaturesOutOfRange(): void
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{
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$this->expectException(InvalidArgumentException::class);
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$this->expectExceptionMessage('Number of features to be preserved should be greater than 0');
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new LDA(null, 0);
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}
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public function testLDAThrowWhenParameterNotSpecified(): void
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{
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$this->expectException(InvalidArgumentException::class);
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$this->expectExceptionMessage('Either totalVariance or numFeatures should be specified in order to run the algorithm');
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new LDA();
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}
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public function testLDAThrowWhenBothParameterSpecified(): void
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{
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$this->expectException(InvalidArgumentException::class);
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$this->expectExceptionMessage('Either totalVariance or numFeatures should be specified in order to run the algorithm');
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new LDA(0.9, 1);
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}
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public function testTransformThrowWhenNotFitted(): void
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{
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$samples = [
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[1, 0],
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[1, 1],
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];
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$pca = new LDA(0.9);
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$this->expectException(InvalidOperationException::class);
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$this->expectExceptionMessage('LDA has not been fitted with respect to original dataset, please run LDA::fit() first');
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$pca->transform($samples);
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}
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}
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