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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\Linear; |
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6 | |||
7 | use Exception; |
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8 | use Phpml\Classification\DecisionTree; |
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9 | use Phpml\Classification\WeightedClassifier; |
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10 | use Phpml\Helper\OneVsRest; |
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11 | use Phpml\Helper\Predictable; |
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12 | use Phpml\Math\Comparison; |
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13 | |||
14 | class DecisionStump extends WeightedClassifier |
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15 | { |
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16 | use Predictable, OneVsRest; |
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17 | |||
18 | public const AUTO_SELECT = -1; |
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19 | |||
20 | /** |
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21 | * @var int |
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22 | */ |
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23 | protected $givenColumnIndex; |
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24 | |||
25 | /** |
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26 | * @var array |
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27 | */ |
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28 | protected $binaryLabels = []; |
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29 | |||
30 | /** |
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31 | * Lowest error rate obtained while training/optimizing the model |
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32 | * |
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33 | * @var float |
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34 | */ |
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35 | protected $trainingErrorRate; |
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36 | |||
37 | /** |
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38 | * @var int |
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39 | */ |
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40 | protected $column; |
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41 | |||
42 | /** |
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43 | * @var mixed |
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44 | */ |
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45 | protected $value; |
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46 | |||
47 | /** |
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48 | * @var string |
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49 | */ |
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50 | protected $operator; |
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51 | |||
52 | /** |
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53 | * @var array |
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54 | */ |
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55 | protected $columnTypes = []; |
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56 | |||
57 | /** |
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58 | * @var int |
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59 | */ |
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60 | protected $featureCount; |
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61 | |||
62 | /** |
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63 | * @var float |
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64 | */ |
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65 | protected $numSplitCount = 100.0; |
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66 | |||
67 | /** |
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68 | * Distribution of samples in the leaves |
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69 | * |
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70 | * @var array |
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71 | */ |
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72 | protected $prob = []; |
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73 | |||
74 | /** |
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75 | * A DecisionStump classifier is a one-level deep DecisionTree. It is generally |
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76 | * used with ensemble algorithms as in the weak classifier role. <br> |
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77 | * |
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78 | * If columnIndex is given, then the stump tries to produce a decision node |
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79 | * on this column, otherwise in cases given the value of -1, the stump itself |
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80 | * decides which column to take for the decision (Default DecisionTree behaviour) |
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81 | */ |
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82 | public function __construct(int $columnIndex = self::AUTO_SELECT) |
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83 | { |
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84 | $this->givenColumnIndex = $columnIndex; |
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85 | } |
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86 | |||
87 | public function __toString(): string |
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88 | { |
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89 | return "IF ${this}->column ${this}->operator ${this}->value ". |
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90 | 'THEN '.$this->binaryLabels[0].' '. |
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91 | 'ELSE '.$this->binaryLabels[1]; |
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92 | } |
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93 | |||
94 | /** |
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95 | * While finding best split point for a numerical valued column, |
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96 | * DecisionStump looks for equally distanced values between minimum and maximum |
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97 | * values in the column. Given <i>$count</i> value determines how many split |
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98 | * points to be probed. The more split counts, the better performance but |
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99 | * worse processing time (Default value is 10.0) |
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100 | */ |
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101 | public function setNumericalSplitCount(float $count): void |
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102 | { |
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103 | $this->numSplitCount = $count; |
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104 | } |
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105 | |||
106 | /** |
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107 | * @throws \Exception |
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108 | */ |
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109 | protected function trainBinary(array $samples, array $targets, array $labels): void |
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110 | { |
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111 | $this->binaryLabels = $labels; |
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112 | $this->featureCount = count($samples[0]); |
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113 | |||
114 | // If a column index is given, it should be among the existing columns |
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115 | if ($this->givenColumnIndex > count($samples[0]) - 1) { |
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116 | $this->givenColumnIndex = self::AUTO_SELECT; |
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117 | } |
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118 | |||
119 | // Check the size of the weights given. |
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120 | // If none given, then assign 1 as a weight to each sample |
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121 | if ($this->weights) { |
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122 | $numWeights = count($this->weights); |
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123 | if ($numWeights != count($samples)) { |
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124 | throw new Exception('Number of sample weights does not match with number of samples'); |
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125 | } |
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126 | } else { |
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127 | $this->weights = array_fill(0, count($samples), 1); |
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128 | } |
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129 | |||
130 | // Determine type of each column as either "continuous" or "nominal" |
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131 | $this->columnTypes = DecisionTree::getColumnTypes($samples); |
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132 | |||
133 | // Try to find the best split in the columns of the dataset |
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134 | // by calculating error rate for each split point in each column |
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135 | $columns = range(0, count($samples[0]) - 1); |
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136 | if ($this->givenColumnIndex != self::AUTO_SELECT) { |
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137 | $columns = [$this->givenColumnIndex]; |
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138 | } |
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139 | |||
140 | $bestSplit = [ |
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141 | 'value' => 0, |
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142 | 'operator' => '', |
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143 | 'prob' => [], |
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144 | 'column' => 0, |
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145 | 'trainingErrorRate' => 1.0, |
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146 | ]; |
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147 | foreach ($columns as $col) { |
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148 | if ($this->columnTypes[$col] == DecisionTree::CONTINUOUS) { |
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149 | $split = $this->getBestNumericalSplit($samples, $targets, $col); |
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150 | } else { |
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151 | $split = $this->getBestNominalSplit($samples, $targets, $col); |
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152 | } |
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153 | |||
154 | if ($split['trainingErrorRate'] < $bestSplit['trainingErrorRate']) { |
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155 | $bestSplit = $split; |
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156 | } |
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157 | } |
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158 | |||
159 | // Assign determined best values to the stump |
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160 | foreach ($bestSplit as $name => $value) { |
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161 | $this->{$name} = $value; |
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162 | } |
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163 | } |
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164 | |||
165 | /** |
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166 | * Determines best split point for the given column |
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167 | */ |
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168 | protected function getBestNumericalSplit(array $samples, array $targets, int $col): array |
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169 | { |
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170 | $values = array_column($samples, $col); |
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171 | // Trying all possible points may be accomplished in two general ways: |
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172 | // 1- Try all values in the $samples array ($values) |
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173 | // 2- Artificially split the range of values into several parts and try them |
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174 | // We choose the second one because it is faster in larger datasets |
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175 | $minValue = min($values); |
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176 | $maxValue = max($values); |
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177 | $stepSize = ($maxValue - $minValue) / $this->numSplitCount; |
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178 | |||
179 | $split = []; |
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180 | |||
181 | foreach (['<=', '>'] as $operator) { |
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182 | // Before trying all possible split points, let's first try |
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183 | // the average value for the cut point |
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184 | $threshold = array_sum($values) / (float) count($values); |
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185 | [$errorRate, $prob] = $this->calculateErrorRate($targets, $threshold, $operator, $values); |
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0 ignored issues
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186 | if ($split === [] || $errorRate < $split['trainingErrorRate']) { |
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187 | $split = [ |
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188 | 'value' => $threshold, |
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189 | 'operator' => $operator, |
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190 | 'prob' => $prob, |
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191 | 'column' => $col, |
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192 | 'trainingErrorRate' => $errorRate, |
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193 | ]; |
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194 | } |
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195 | |||
196 | // Try other possible points one by one |
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197 | for ($step = $minValue; $step <= $maxValue; $step += $stepSize) { |
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198 | $threshold = (float) $step; |
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199 | [$errorRate, $prob] = $this->calculateErrorRate($targets, $threshold, $operator, $values); |
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200 | if ($errorRate < $split['trainingErrorRate']) { |
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201 | $split = [ |
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202 | 'value' => $threshold, |
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203 | 'operator' => $operator, |
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204 | 'prob' => $prob, |
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205 | 'column' => $col, |
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206 | 'trainingErrorRate' => $errorRate, |
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207 | ]; |
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208 | } |
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209 | }// for |
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210 | } |
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211 | |||
212 | return $split; |
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213 | } |
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214 | |||
215 | protected function getBestNominalSplit(array $samples, array $targets, int $col): array |
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216 | { |
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217 | $values = array_column($samples, $col); |
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218 | $valueCounts = array_count_values($values); |
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219 | $distinctVals = array_keys($valueCounts); |
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220 | |||
221 | $split = []; |
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222 | |||
223 | foreach (['=', '!='] as $operator) { |
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224 | foreach ($distinctVals as $val) { |
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225 | [$errorRate, $prob] = $this->calculateErrorRate($targets, $val, $operator, $values); |
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0 ignored issues
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226 | |||
227 | if ($split === [] || $split['trainingErrorRate'] < $errorRate) { |
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228 | $split = [ |
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229 | 'value' => $val, |
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230 | 'operator' => $operator, |
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231 | 'prob' => $prob, |
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232 | 'column' => $col, |
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233 | 'trainingErrorRate' => $errorRate, |
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234 | ]; |
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235 | } |
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236 | } |
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237 | } |
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238 | |||
239 | return $split; |
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240 | } |
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241 | |||
242 | /** |
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243 | * Calculates the ratio of wrong predictions based on the new threshold |
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244 | * value given as the parameter |
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245 | */ |
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246 | protected function calculateErrorRate(array $targets, float $threshold, string $operator, array $values): array |
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247 | { |
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248 | $wrong = 0.0; |
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249 | $prob = []; |
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250 | $leftLabel = $this->binaryLabels[0]; |
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251 | $rightLabel = $this->binaryLabels[1]; |
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252 | |||
253 | foreach ($values as $index => $value) { |
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254 | if (Comparison::compare($value, $threshold, $operator)) { |
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255 | $predicted = $leftLabel; |
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256 | } else { |
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257 | $predicted = $rightLabel; |
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258 | } |
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259 | |||
260 | $target = $targets[$index]; |
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261 | if ((string) $predicted != (string) $targets[$index]) { |
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262 | $wrong += $this->weights[$index]; |
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263 | } |
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264 | |||
265 | if (!isset($prob[$predicted][$target])) { |
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266 | $prob[$predicted][$target] = 0; |
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267 | } |
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268 | |||
269 | ++$prob[$predicted][$target]; |
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270 | } |
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271 | |||
272 | // Calculate probabilities: Proportion of labels in each leaf |
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273 | $dist = array_combine($this->binaryLabels, array_fill(0, 2, 0.0)); |
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274 | foreach ($prob as $leaf => $counts) { |
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275 | $leafTotal = (float) array_sum($prob[$leaf]); |
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276 | foreach ($counts as $label => $count) { |
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277 | if ((string) $leaf == (string) $label) { |
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278 | $dist[$leaf] = $count / $leafTotal; |
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279 | } |
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280 | } |
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281 | } |
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282 | |||
283 | return [$wrong / (float) array_sum($this->weights), $dist]; |
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284 | } |
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285 | |||
286 | /** |
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287 | * Returns the probability of the sample of belonging to the given label |
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288 | * |
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289 | * Probability of a sample is calculated as the proportion of the label |
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290 | * within the labels of the training samples in the decision node |
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291 | * |
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292 | * @param mixed $label |
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293 | */ |
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294 | protected function predictProbability(array $sample, $label): float |
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295 | { |
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296 | $predicted = $this->predictSampleBinary($sample); |
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297 | if ((string) $predicted == (string) $label) { |
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298 | return $this->prob[$label]; |
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299 | } |
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300 | |||
301 | return 0.0; |
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302 | } |
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303 | |||
304 | /** |
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305 | * @return mixed |
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306 | */ |
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307 | protected function predictSampleBinary(array $sample) |
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308 | { |
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309 | if (Comparison::compare($sample[$this->column], $this->value, $this->operator)) { |
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310 | return $this->binaryLabels[0]; |
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311 | } |
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312 | |||
313 | return $this->binaryLabels[1]; |
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314 | } |
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315 | |||
316 | protected function resetBinary(): void |
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317 | { |
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318 | } |
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319 | } |
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320 |
This check marks access to variables or properties that have not been declared yet. While PHP has no explicit notion of declaring a variable, accessing it before a value is assigned to it is most likely a bug.