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Long Term Support Release
Differences Between: [Versions 39 and 403]
1 <?php 2 3 declare(strict_types=1); 4 5 namespace Phpml\Classification\Ensemble; 6 7 use Phpml\Classification\Classifier; 8 use Phpml\Classification\DecisionTree; 9 use Phpml\Exception\InvalidArgumentException; 10 use Phpml\Helper\Predictable; 11 use Phpml\Helper\Trainable; 12 use ReflectionClass; 13 14 class Bagging implements Classifier 15 { 16 use Trainable; 17 use Predictable; 18 19 /** 20 * @var int 21 */ 22 protected $numSamples; 23 24 /** 25 * @var int 26 */ 27 protected $featureCount = 0; 28 29 /** 30 * @var int 31 */ 32 protected $numClassifier; 33 34 /** 35 * @var string 36 */ 37 protected $classifier = DecisionTree::class; 38 39 /** 40 * @var array 41 */ 42 protected $classifierOptions = ['depth' => 20]; 43 44 /** 45 * @var array 46 */ 47 protected $classifiers = []; 48 49 /** 50 * @var float 51 */ 52 protected $subsetRatio = 0.7; 53 54 /** 55 * Creates an ensemble classifier with given number of base classifiers 56 * Default number of base classifiers is 50. 57 * The more number of base classifiers, the better performance but at the cost of procesing time 58 */ 59 public function __construct(int $numClassifier = 50) 60 { 61 $this->numClassifier = $numClassifier; 62 } 63 64 /** 65 * This method determines the ratio of samples used to create the 'bootstrap' subset, 66 * e.g., random samples drawn from the original dataset with replacement (allow repeats), 67 * to train each base classifier. 68 * 69 * @return $this 70 * 71 * @throws InvalidArgumentException 72 */ 73 public function setSubsetRatio(float $ratio) 74 { 75 if ($ratio < 0.1 || $ratio > 1.0) { 76 throw new InvalidArgumentException('Subset ratio should be between 0.1 and 1.0'); 77 } 78 79 $this->subsetRatio = $ratio; 80 81 return $this; 82 } 83 84 /** 85 * This method is used to set the base classifier. Default value is 86 * DecisionTree::class, but any class that implements the <i>Classifier</i> 87 * can be used. <br> 88 * While giving the parameters of the classifier, the values should be 89 * given in the order they are in the constructor of the classifier and parameter 90 * names are neglected. 91 * 92 * @return $this 93 */ 94 public function setClassifer(string $classifier, array $classifierOptions = []) 95 { 96 $this->classifier = $classifier; 97 $this->classifierOptions = $classifierOptions; 98 99 return $this; 100 } 101 102 public function train(array $samples, array $targets): void 103 { 104 $this->samples = array_merge($this->samples, $samples); 105 $this->targets = array_merge($this->targets, $targets); 106 $this->featureCount = count($samples[0]); 107 $this->numSamples = count($this->samples); 108 109 // Init classifiers and train them with bootstrap samples 110 $this->classifiers = $this->initClassifiers(); 111 $index = 0; 112 foreach ($this->classifiers as $classifier) { 113 [$samples, $targets] = $this->getRandomSubset($index); 114 $classifier->train($samples, $targets); 115 ++$index; 116 } 117 } 118 119 protected function getRandomSubset(int $index): array 120 { 121 $samples = []; 122 $targets = []; 123 srand($index); 124 $bootstrapSize = $this->subsetRatio * $this->numSamples; 125 for ($i = 0; $i < $bootstrapSize; ++$i) { 126 $rand = random_int(0, $this->numSamples - 1); 127 $samples[] = $this->samples[$rand]; 128 $targets[] = $this->targets[$rand]; 129 } 130 131 return [$samples, $targets]; 132 } 133 134 protected function initClassifiers(): array 135 { 136 $classifiers = []; 137 for ($i = 0; $i < $this->numClassifier; ++$i) { 138 $ref = new ReflectionClass($this->classifier); 139 /** @var Classifier $obj */ 140 $obj = count($this->classifierOptions) === 0 ? $ref->newInstance() : $ref->newInstanceArgs($this->classifierOptions); 141 142 $classifiers[] = $this->initSingleClassifier($obj); 143 } 144 145 return $classifiers; 146 } 147 148 protected function initSingleClassifier(Classifier $classifier): Classifier 149 { 150 return $classifier; 151 } 152 153 /** 154 * @return mixed 155 */ 156 protected function predictSample(array $sample) 157 { 158 $predictions = []; 159 foreach ($this->classifiers as $classifier) { 160 /** @var Classifier $classifier */ 161 $predictions[] = $classifier->predict($sample); 162 } 163 164 $counts = array_count_values($predictions); 165 arsort($counts); 166 reset($counts); 167 168 return key($counts); 169 } 170 }
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