1 | <?php |
||
2 | |||
3 | declare(strict_types=1); |
||
4 | |||
5 | namespace Phpml\Helper; |
||
6 | |||
7 | use Phpml\Classification\Classifier; |
||
8 | |||
9 | trait OneVsRest |
||
10 | { |
||
11 | /** |
||
12 | * @var array |
||
13 | */ |
||
14 | protected $classifiers = []; |
||
15 | |||
16 | /** |
||
17 | * All provided training targets' labels. |
||
18 | * |
||
19 | * @var array |
||
20 | */ |
||
21 | protected $allLabels = []; |
||
22 | |||
23 | /** |
||
24 | * @var array |
||
25 | */ |
||
26 | protected $costValues = []; |
||
27 | |||
28 | /** |
||
29 | * Train a binary classifier in the OvR style |
||
30 | */ |
||
31 | public function train(array $samples, array $targets): void |
||
32 | { |
||
33 | // Clears previous stuff. |
||
34 | $this->reset(); |
||
35 | |||
36 | $this->trainByLabel($samples, $targets); |
||
37 | } |
||
38 | |||
39 | /** |
||
40 | * Resets the classifier and the vars internally used by OneVsRest to create multiple classifiers. |
||
41 | */ |
||
42 | public function reset(): void |
||
43 | { |
||
44 | $this->classifiers = []; |
||
45 | $this->allLabels = []; |
||
46 | $this->costValues = []; |
||
47 | |||
48 | $this->resetBinary(); |
||
49 | } |
||
50 | |||
51 | protected function trainByLabel(array $samples, array $targets, array $allLabels = []): void |
||
52 | { |
||
53 | // Overwrites the current value if it exist. $allLabels must be provided for each partialTrain run. |
||
54 | $this->allLabels = count($allLabels) === 0 ? array_keys(array_count_values($targets)) : $allLabels; |
||
55 | sort($this->allLabels, SORT_STRING); |
||
56 | |||
57 | // If there are only two targets, then there is no need to perform OvR |
||
58 | if (count($this->allLabels) === 2) { |
||
59 | // Init classifier if required. |
||
60 | if (count($this->classifiers) === 0) { |
||
61 | $this->classifiers[0] = $this->getClassifierCopy(); |
||
62 | } |
||
63 | |||
64 | $this->classifiers[0]->trainBinary($samples, $targets, $this->allLabels); |
||
65 | } else { |
||
66 | // Train a separate classifier for each label and memorize them |
||
67 | |||
68 | foreach ($this->allLabels as $label) { |
||
69 | // Init classifier if required. |
||
70 | if (!isset($this->classifiers[$label])) { |
||
71 | $this->classifiers[$label] = $this->getClassifierCopy(); |
||
72 | } |
||
73 | |||
74 | [$binarizedTargets, $classifierLabels] = $this->binarizeTargets($targets, $label); |
||
75 | $this->classifiers[$label]->trainBinary($samples, $binarizedTargets, $classifierLabels); |
||
76 | } |
||
77 | } |
||
78 | |||
79 | // If the underlying classifier is capable of giving the cost values |
||
80 | // during the training, then assign it to the relevant variable |
||
81 | // Adding just the first classifier cost values to avoid complex average calculations. |
||
82 | $classifierref = reset($this->classifiers); |
||
83 | if (method_exists($classifierref, 'getCostValues')) { |
||
84 | $this->costValues = $classifierref->getCostValues(); |
||
85 | } |
||
86 | } |
||
87 | |||
88 | /** |
||
89 | * Returns an instance of the current class after cleaning up OneVsRest stuff. |
||
90 | */ |
||
91 | protected function getClassifierCopy(): Classifier |
||
92 | { |
||
93 | // Clone the current classifier, so that |
||
94 | // we don't mess up its variables while training |
||
95 | // multiple instances of this classifier |
||
96 | $classifier = clone $this; |
||
97 | $classifier->reset(); |
||
98 | |||
99 | return $classifier; |
||
0 ignored issues
–
show
Bug
Best Practice
introduced
by
Loading history...
|
|||
100 | } |
||
101 | |||
102 | /** |
||
103 | * @return mixed |
||
104 | */ |
||
105 | protected function predictSample(array $sample) |
||
106 | { |
||
107 | if (count($this->allLabels) === 2) { |
||
108 | return $this->classifiers[0]->predictSampleBinary($sample); |
||
109 | } |
||
110 | |||
111 | $probs = []; |
||
112 | |||
113 | foreach ($this->classifiers as $label => $predictor) { |
||
114 | $probs[$label] = $predictor->predictProbability($sample, $label); |
||
115 | } |
||
116 | |||
117 | arsort($probs, SORT_NUMERIC); |
||
118 | |||
119 | return key($probs); |
||
120 | } |
||
121 | |||
122 | /** |
||
123 | * Each classifier should implement this method instead of train(samples, targets) |
||
124 | */ |
||
125 | abstract protected function trainBinary(array $samples, array $targets, array $labels); |
||
126 | |||
127 | /** |
||
128 | * To be overwritten by OneVsRest classifiers. |
||
129 | */ |
||
130 | abstract protected function resetBinary(): void; |
||
131 | |||
132 | /** |
||
133 | * Each classifier that make use of OvR approach should be able to |
||
134 | * return a probability for a sample to belong to the given label. |
||
135 | * |
||
136 | * @return mixed |
||
137 | */ |
||
138 | abstract protected function predictProbability(array $sample, string $label); |
||
139 | |||
140 | /** |
||
141 | * Each classifier should implement this method instead of predictSample() |
||
142 | * |
||
143 | * @return mixed |
||
144 | */ |
||
145 | abstract protected function predictSampleBinary(array $sample); |
||
146 | |||
147 | /** |
||
148 | * Groups all targets into two groups: Targets equal to |
||
149 | * the given label and the others |
||
150 | * |
||
151 | * $targets is not passed by reference nor contains objects so this method |
||
152 | * changes will not affect the caller $targets array. |
||
153 | * |
||
154 | * @param mixed $label |
||
155 | * |
||
156 | * @return array Binarized targets and target's labels |
||
157 | */ |
||
158 | private function binarizeTargets(array $targets, $label): array |
||
159 | { |
||
160 | $notLabel = "not_${label}"; |
||
161 | foreach ($targets as $key => $target) { |
||
162 | $targets[$key] = $target == $label ? $label : $notLabel; |
||
163 | } |
||
164 | |||
165 | $labels = [$label, $notLabel]; |
||
166 | |||
167 | return [$targets, $labels]; |
||
168 | } |
||
169 | } |
||
170 |