php-ml/tests/DimensionReduction/LDATest.php

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