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Differences Between: [Versions 310 and 400] [Versions 311 and 400] [Versions 39 and 400]

   1  <?php
   2  
   3  declare(strict_types=1);
   4  
   5  namespace Phpml\FeatureSelection\ScoringFunction;
   6  
   7  use Phpml\FeatureSelection\ScoringFunction;
   8  use Phpml\Math\Matrix;
   9  use Phpml\Math\Statistic\Mean;
  10  
  11  /**
  12   * Quick linear model for testing the effect of a single regressor,
  13   * sequentially for many regressors.
  14   *
  15   * This is done in 2 steps:
  16   *
  17   * 1. The cross correlation between each regressor and the target is computed,
  18   * that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *std(y)).
  19   * 2. It is converted to an F score.
  20   *
  21   * 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)
  22   */
  23  final class UnivariateLinearRegression implements ScoringFunction
  24  {
  25      /**
  26       * @var bool
  27       */
  28      private $center;
  29  
  30      /**
  31       * @param bool $center - if true samples and targets will be centered
  32       */
  33      public function __construct(bool $center = true)
  34      {
  35          $this->center = $center;
  36      }
  37  
  38      public function score(array $samples, array $targets): array
  39      {
  40          if ($this->center) {
  41              $this->centerTargets($targets);
  42              $this->centerSamples($samples);
  43          }
  44  
  45          $correlations = [];
  46          foreach (array_keys($samples[0]) as $index) {
  47              $featureColumn = array_column($samples, $index);
  48              $correlations[$index] =
  49                  Matrix::dot($targets, $featureColumn)[0] / (new Matrix($featureColumn, false))->transpose()->frobeniusNorm()
  50                  / (new Matrix($targets, false))->frobeniusNorm();
  51          }
  52  
  53          $degreesOfFreedom = count($targets) - ($this->center ? 2 : 1);
  54  
  55          return array_map(function (float $correlation) use ($degreesOfFreedom): float {
  56              return $correlation ** 2 / (1 - $correlation ** 2) * $degreesOfFreedom;
  57          }, $correlations);
  58      }
  59  
  60      private function centerTargets(array &$targets): void
  61      {
  62          $mean = Mean::arithmetic($targets);
  63          array_walk($targets, function (&$target) use ($mean): void {
  64              $target -= $mean;
  65          });
  66      }
  67  
  68      private function centerSamples(array &$samples): void
  69      {
  70          $means = [];
  71          foreach ($samples[0] as $index => $feature) {
  72              $means[$index] = Mean::arithmetic(array_column($samples, $index));
  73          }
  74  
  75          foreach ($samples as &$sample) {
  76              foreach ($sample as $index => &$feature) {
  77                  $feature -= $means[$index];
  78              }
  79          }
  80      }
  81  }