82 lines
2.5 KiB
PHP
82 lines
2.5 KiB
PHP
<?php
|
|
|
|
declare(strict_types=1);
|
|
|
|
namespace Phpml\FeatureSelection\ScoringFunction;
|
|
|
|
use Phpml\FeatureSelection\ScoringFunction;
|
|
use Phpml\Math\Matrix;
|
|
use Phpml\Math\Statistic\Mean;
|
|
|
|
/**
|
|
* Quick linear model for testing the effect of a single regressor,
|
|
* sequentially for many regressors.
|
|
*
|
|
* This is done in 2 steps:
|
|
*
|
|
* 1. The cross correlation between each regressor and the target is computed,
|
|
* that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *std(y)).
|
|
* 2. It is converted to an F score.
|
|
*
|
|
* Ported from scikit-learn f_regression function (http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html#sklearn.feature_selection.f_regression)
|
|
*/
|
|
final class UnivariateLinearRegression implements ScoringFunction
|
|
{
|
|
/**
|
|
* @var bool
|
|
*/
|
|
private $center;
|
|
|
|
/**
|
|
* @param bool $center - if true samples and targets will be centered
|
|
*/
|
|
public function __construct(bool $center = true)
|
|
{
|
|
$this->center = $center;
|
|
}
|
|
|
|
public function score(array $samples, array $targets): array
|
|
{
|
|
if ($this->center) {
|
|
$this->centerTargets($targets);
|
|
$this->centerSamples($samples);
|
|
}
|
|
|
|
$correlations = [];
|
|
foreach (array_keys($samples[0]) as $index) {
|
|
$featureColumn = array_column($samples, $index);
|
|
$correlations[$index] =
|
|
Matrix::dot($targets, $featureColumn)[0] / (new Matrix($featureColumn, false))->transpose()->frobeniusNorm()
|
|
/ (new Matrix($targets, false))->frobeniusNorm();
|
|
}
|
|
|
|
$degreesOfFreedom = count($targets) - ($this->center ? 2 : 1);
|
|
|
|
return array_map(function (float $correlation) use ($degreesOfFreedom): float {
|
|
return $correlation ** 2 / (1 - $correlation ** 2) * $degreesOfFreedom;
|
|
}, $correlations);
|
|
}
|
|
|
|
private function centerTargets(array &$targets): void
|
|
{
|
|
$mean = Mean::arithmetic($targets);
|
|
array_walk($targets, function (&$target) use ($mean): void {
|
|
$target -= $mean;
|
|
});
|
|
}
|
|
|
|
private function centerSamples(array &$samples): void
|
|
{
|
|
$means = [];
|
|
foreach ($samples[0] as $index => $feature) {
|
|
$means[$index] = Mean::arithmetic(array_column($samples, $index));
|
|
}
|
|
|
|
foreach ($samples as &$sample) {
|
|
foreach ($sample as $index => &$feature) {
|
|
$feature -= $means[$index];
|
|
}
|
|
}
|
|
}
|
|
}
|