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1 | <?php |
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2 | |||
3 | declare(strict_types=1); |
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4 | |||
5 | namespace Phpml\Classification\Ensemble; |
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6 | |||
7 | use Exception; |
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8 | use Phpml\Classification\Classifier; |
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9 | use Phpml\Classification\DecisionTree; |
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10 | use Phpml\Helper\Predictable; |
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11 | use Phpml\Helper\Trainable; |
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12 | use ReflectionClass; |
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13 | |||
14 | class Bagging implements Classifier |
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15 | { |
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16 | use Trainable, Predictable; |
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17 | |||
18 | /** |
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19 | * @var int |
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20 | */ |
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21 | protected $numSamples; |
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22 | |||
23 | /** |
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24 | * @var int |
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25 | */ |
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26 | protected $featureCount = 0; |
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27 | |||
28 | /** |
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29 | * @var int |
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30 | */ |
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31 | protected $numClassifier; |
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32 | |||
33 | /** |
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34 | * @var Classifier |
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35 | */ |
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36 | protected $classifier = DecisionTree::class; |
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37 | |||
38 | /** |
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39 | * @var array |
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40 | */ |
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41 | protected $classifierOptions = ['depth' => 20]; |
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42 | |||
43 | /** |
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44 | * @var array |
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45 | */ |
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46 | protected $classifiers = []; |
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47 | |||
48 | /** |
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49 | * @var float |
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50 | */ |
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51 | protected $subsetRatio = 0.7; |
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52 | |||
53 | /** |
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54 | * @var array |
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55 | */ |
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56 | private $targets = []; |
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57 | |||
58 | /** |
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59 | * @var array |
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60 | */ |
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61 | private $samples = []; |
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62 | |||
63 | /** |
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64 | * Creates an ensemble classifier with given number of base classifiers |
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65 | * Default number of base classifiers is 50. |
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66 | * The more number of base classifiers, the better performance but at the cost of procesing time |
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67 | */ |
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68 | public function __construct(int $numClassifier = 50) |
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69 | { |
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70 | $this->numClassifier = $numClassifier; |
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71 | } |
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72 | |||
73 | /** |
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74 | * This method determines the ratio of samples used to create the 'bootstrap' subset, |
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75 | * e.g., random samples drawn from the original dataset with replacement (allow repeats), |
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76 | * to train each base classifier. |
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77 | * |
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78 | * @return $this |
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79 | * |
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80 | * @throws \Exception |
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81 | */ |
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82 | public function setSubsetRatio(float $ratio) |
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83 | { |
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84 | if ($ratio < 0.1 || $ratio > 1.0) { |
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85 | throw new Exception('Subset ratio should be between 0.1 and 1.0'); |
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86 | } |
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87 | |||
88 | $this->subsetRatio = $ratio; |
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89 | |||
90 | return $this; |
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91 | } |
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92 | |||
93 | /** |
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94 | * This method is used to set the base classifier. Default value is |
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95 | * DecisionTree::class, but any class that implements the <i>Classifier</i> |
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96 | * can be used. <br> |
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97 | * While giving the parameters of the classifier, the values should be |
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98 | * given in the order they are in the constructor of the classifier and parameter |
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99 | * names are neglected. |
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100 | * |
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101 | * @return $this |
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102 | */ |
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103 | public function setClassifer(string $classifier, array $classifierOptions = []) |
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104 | { |
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105 | $this->classifier = $classifier; |
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106 | $this->classifierOptions = $classifierOptions; |
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107 | |||
108 | return $this; |
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109 | } |
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110 | |||
111 | public function train(array $samples, array $targets): void |
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112 | { |
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113 | $this->samples = array_merge($this->samples, $samples); |
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114 | $this->targets = array_merge($this->targets, $targets); |
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115 | $this->featureCount = count($samples[0]); |
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116 | $this->numSamples = count($this->samples); |
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117 | |||
118 | // Init classifiers and train them with bootstrap samples |
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119 | $this->classifiers = $this->initClassifiers(); |
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120 | $index = 0; |
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121 | foreach ($this->classifiers as $classifier) { |
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122 | [$samples, $targets] = $this->getRandomSubset($index); |
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123 | $classifier->train($samples, $targets); |
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124 | ++$index; |
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125 | } |
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126 | } |
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127 | |||
128 | protected function getRandomSubset(int $index): array |
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129 | { |
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130 | $samples = []; |
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131 | $targets = []; |
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132 | srand($index); |
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133 | $bootstrapSize = $this->subsetRatio * $this->numSamples; |
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134 | for ($i = 0; $i < $bootstrapSize; ++$i) { |
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135 | $rand = random_int(0, $this->numSamples - 1); |
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136 | $samples[] = $this->samples[$rand]; |
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137 | $targets[] = $this->targets[$rand]; |
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138 | } |
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139 | |||
140 | return [$samples, $targets]; |
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141 | } |
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142 | |||
143 | protected function initClassifiers(): array |
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144 | { |
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145 | $classifiers = []; |
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146 | for ($i = 0; $i < $this->numClassifier; ++$i) { |
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147 | $ref = new ReflectionClass($this->classifier); |
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148 | if ($this->classifierOptions) { |
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149 | $obj = $ref->newInstanceArgs($this->classifierOptions); |
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150 | } else { |
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151 | $obj = $ref->newInstance(); |
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152 | } |
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153 | |||
154 | $classifiers[] = $this->initSingleClassifier($obj); |
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155 | } |
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156 | |||
157 | return $classifiers; |
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158 | } |
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159 | |||
160 | protected function initSingleClassifier(Classifier $classifier): Classifier |
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161 | { |
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162 | return $classifier; |
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163 | } |
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164 | |||
165 | /** |
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166 | * @return mixed |
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167 | */ |
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168 | protected function predictSample(array $sample) |
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169 | { |
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170 | $predictions = []; |
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171 | foreach ($this->classifiers as $classifier) { |
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172 | /* @var $classifier Classifier */ |
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173 | $predictions[] = $classifier->predict($sample); |
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174 | } |
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175 | |||
176 | $counts = array_count_values($predictions); |
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177 | arsort($counts); |
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178 | reset($counts); |
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179 | |||
180 | return key($counts); |
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181 | } |
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182 | } |
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183 |
Our type inference engine has found an assignment to a property that is incompatible with the declared type of that property.
Either this assignment is in error or the assigned type should be added to the documentation/type hint for that property..