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Differences Between: [Versions 310 and 400] [Versions 310 and 401] [Versions 310 and 402] [Versions 310 and 403]

   1  <?php
   2  
   3  declare(strict_types=1);
   4  
   5  namespace Phpml\Classification\Linear;
   6  
   7  use Closure;
   8  use Phpml\Classification\Classifier;
   9  use Phpml\Exception\InvalidArgumentException;
  10  use Phpml\Helper\OneVsRest;
  11  use Phpml\Helper\Optimizer\GD;
  12  use Phpml\Helper\Optimizer\Optimizer;
  13  use Phpml\Helper\Optimizer\StochasticGD;
  14  use Phpml\Helper\Predictable;
  15  use Phpml\IncrementalEstimator;
  16  use Phpml\Preprocessing\Normalizer;
  17  
  18  class Perceptron implements Classifier, IncrementalEstimator
  19  {
  20      use Predictable;
  21      use OneVsRest;
  22  
  23      /**
  24       * @var Optimizer|GD|StochasticGD|null
  25       */
  26      protected $optimizer;
  27  
  28      /**
  29       * @var array
  30       */
  31      protected $labels = [];
  32  
  33      /**
  34       * @var int
  35       */
  36      protected $featureCount = 0;
  37  
  38      /**
  39       * @var array
  40       */
  41      protected $weights = [];
  42  
  43      /**
  44       * @var float
  45       */
  46      protected $learningRate;
  47  
  48      /**
  49       * @var int
  50       */
  51      protected $maxIterations;
  52  
  53      /**
  54       * @var Normalizer
  55       */
  56      protected $normalizer;
  57  
  58      /**
  59       * @var bool
  60       */
  61      protected $enableEarlyStop = true;
  62  
  63      /**
  64       * Initalize a perceptron classifier with given learning rate and maximum
  65       * number of iterations used while training the perceptron
  66       *
  67       * @param float $learningRate  Value between 0.0(exclusive) and 1.0(inclusive)
  68       * @param int   $maxIterations Must be at least 1
  69       *
  70       * @throws InvalidArgumentException
  71       */
  72      public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true)
  73      {
  74          if ($learningRate <= 0.0 || $learningRate > 1.0) {
  75              throw new InvalidArgumentException('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
  76          }
  77  
  78          if ($maxIterations <= 0) {
  79              throw new InvalidArgumentException('Maximum number of iterations must be an integer greater than 0');
  80          }
  81  
  82          if ($normalizeInputs) {
  83              $this->normalizer = new Normalizer(Normalizer::NORM_STD);
  84          }
  85  
  86          $this->learningRate = $learningRate;
  87          $this->maxIterations = $maxIterations;
  88      }
  89  
  90      public function partialTrain(array $samples, array $targets, array $labels = []): void
  91      {
  92          $this->trainByLabel($samples, $targets, $labels);
  93      }
  94  
  95      public function trainBinary(array $samples, array $targets, array $labels): void
  96      {
  97          if ($this->normalizer !== null) {
  98              $this->normalizer->transform($samples);
  99          }
 100  
 101          // Set all target values to either -1 or 1
 102          $this->labels = [
 103              1 => $labels[0],
 104              -1 => $labels[1],
 105          ];
 106          foreach ($targets as $key => $target) {
 107              $targets[$key] = (string) $target == (string) $this->labels[1] ? 1 : -1;
 108          }
 109  
 110          // Set samples and feature count vars
 111          $this->featureCount = count($samples[0]);
 112  
 113          $this->runTraining($samples, $targets);
 114      }
 115  
 116      /**
 117       * Normally enabling early stopping for the optimization procedure may
 118       * help saving processing time while in some cases it may result in
 119       * premature convergence.<br>
 120       *
 121       * If "false" is given, the optimization procedure will always be executed
 122       * for $maxIterations times
 123       *
 124       * @return $this
 125       */
 126      public function setEarlyStop(bool $enable = true)
 127      {
 128          $this->enableEarlyStop = $enable;
 129  
 130          return $this;
 131      }
 132  
 133      /**
 134       * Returns the cost values obtained during the training.
 135       */
 136      public function getCostValues(): array
 137      {
 138          return $this->costValues;
 139      }
 140  
 141      protected function resetBinary(): void
 142      {
 143          $this->labels = [];
 144          $this->optimizer = null;
 145          $this->featureCount = 0;
 146          $this->weights = [];
 147          $this->costValues = [];
 148      }
 149  
 150      /**
 151       * Trains the perceptron model with Stochastic Gradient Descent optimization
 152       * to get the correct set of weights
 153       */
 154      protected function runTraining(array $samples, array $targets): void
 155      {
 156          // The cost function is the sum of squares
 157          $callback = function ($weights, $sample, $target) {
 158              $this->weights = $weights;
 159  
 160              $prediction = $this->outputClass($sample);
 161              $gradient = $prediction - $target;
 162              $error = $gradient ** 2;
 163  
 164              return [$error, $gradient];
 165          };
 166  
 167          $this->runGradientDescent($samples, $targets, $callback);
 168      }
 169  
 170      /**
 171       * Executes a Gradient Descent algorithm for
 172       * the given cost function
 173       */
 174      protected function runGradientDescent(array $samples, array $targets, Closure $gradientFunc, bool $isBatch = false): void
 175      {
 176          $class = $isBatch ? GD::class : StochasticGD::class;
 177  
 178          if ($this->optimizer === null) {
 179              $this->optimizer = (new $class($this->featureCount))
 180                  ->setLearningRate($this->learningRate)
 181                  ->setMaxIterations($this->maxIterations)
 182                  ->setChangeThreshold(1e-6)
 183                  ->setEarlyStop($this->enableEarlyStop);
 184          }
 185  
 186          $this->weights = $this->optimizer->runOptimization($samples, $targets, $gradientFunc);
 187          $this->costValues = $this->optimizer->getCostValues();
 188      }
 189  
 190      /**
 191       * Checks if the sample should be normalized and if so, returns the
 192       * normalized sample
 193       */
 194      protected function checkNormalizedSample(array $sample): array
 195      {
 196          if ($this->normalizer !== null) {
 197              $samples = [$sample];
 198              $this->normalizer->transform($samples);
 199              $sample = $samples[0];
 200          }
 201  
 202          return $sample;
 203      }
 204  
 205      /**
 206       * Calculates net output of the network as a float value for the given input
 207       *
 208       * @return int|float
 209       */
 210      protected function output(array $sample)
 211      {
 212          $sum = 0;
 213          foreach ($this->weights as $index => $w) {
 214              if ($index == 0) {
 215                  $sum += $w;
 216              } else {
 217                  $sum += $w * $sample[$index - 1];
 218              }
 219          }
 220  
 221          return $sum;
 222      }
 223  
 224      /**
 225       * Returns the class value (either -1 or 1) for the given input
 226       */
 227      protected function outputClass(array $sample): int
 228      {
 229          return $this->output($sample) > 0 ? 1 : -1;
 230      }
 231  
 232      /**
 233       * Returns the probability of the sample of belonging to the given label.
 234       *
 235       * The probability is simply taken as the distance of the sample
 236       * to the decision plane.
 237       *
 238       * @param mixed $label
 239       */
 240      protected function predictProbability(array $sample, $label): float
 241      {
 242          $predicted = $this->predictSampleBinary($sample);
 243  
 244          if ((string) $predicted == (string) $label) {
 245              $sample = $this->checkNormalizedSample($sample);
 246  
 247              return (float) abs($this->output($sample));
 248          }
 249  
 250          return 0.0;
 251      }
 252  
 253      /**
 254       * @return mixed
 255       */
 256      protected function predictSampleBinary(array $sample)
 257      {
 258          $sample = $this->checkNormalizedSample($sample);
 259  
 260          $predictedClass = $this->outputClass($sample);
 261  
 262          return $this->labels[$predictedClass];
 263      }
 264  }