PDF rausgenommen

msd2/myoos/vendor/php-ai/php-ml/src/Helper/OneVsRest.php

@@ -0,0 +1,169 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Phpml\Helper;
+
+use Phpml\Classification\Classifier;
+
+trait OneVsRest
+{
+    /**
+     * @var array
+     */
+    protected $classifiers = [];
+
+    /**
+     * All provided training targets' labels.
+     *
+     * @var array
+     */
+    protected $allLabels = [];
+
+    /**
+     * @var array
+     */
+    protected $costValues = [];
+
+    /**
+     * Train a binary classifier in the OvR style
+     */
+    public function train(array $samples, array $targets): void
+    {
+        // Clears previous stuff.
+        $this->reset();
+
+        $this->trainByLabel($samples, $targets);
+    }
+
+    /**
+     * Resets the classifier and the vars internally used by OneVsRest to create multiple classifiers.
+     */
+    public function reset(): void
+    {
+        $this->classifiers = [];
+        $this->allLabels = [];
+        $this->costValues = [];
+
+        $this->resetBinary();
+    }
+
+    protected function trainByLabel(array $samples, array $targets, array $allLabels = []): void
+    {
+        // Overwrites the current value if it exist. $allLabels must be provided for each partialTrain run.
+        $this->allLabels = count($allLabels) === 0 ? array_keys(array_count_values($targets)) : $allLabels;
+        sort($this->allLabels, SORT_STRING);
+
+        // If there are only two targets, then there is no need to perform OvR
+        if (count($this->allLabels) === 2) {
+            // Init classifier if required.
+            if (count($this->classifiers) === 0) {
+                $this->classifiers[0] = $this->getClassifierCopy();
+            }
+
+            $this->classifiers[0]->trainBinary($samples, $targets, $this->allLabels);
+        } else {
+            // Train a separate classifier for each label and memorize them
+
+            foreach ($this->allLabels as $label) {
+                // Init classifier if required.
+                if (!isset($this->classifiers[$label])) {
+                    $this->classifiers[$label] = $this->getClassifierCopy();
+                }
+
+                [$binarizedTargets, $classifierLabels] = $this->binarizeTargets($targets, $label);
+                $this->classifiers[$label]->trainBinary($samples, $binarizedTargets, $classifierLabels);
+            }
+        }
+
+        // If the underlying classifier is capable of giving the cost values
+        // during the training, then assign it to the relevant variable
+        // Adding just the first classifier cost values to avoid complex average calculations.
+        $classifierref = reset($this->classifiers);
+        if (method_exists($classifierref, 'getCostValues')) {
+            $this->costValues = $classifierref->getCostValues();
+        }
+    }
+
+    /**
+     * Returns an instance of the current class after cleaning up OneVsRest stuff.
+     */
+    protected function getClassifierCopy(): Classifier
+    {
+        // Clone the current classifier, so that
+        // we don't mess up its variables while training
+        // multiple instances of this classifier
+        $classifier = clone $this;
+        $classifier->reset();
+
+        return $classifier;
+    }
+
+    /**
+     * @return mixed
+     */
+    protected function predictSample(array $sample)
+    {
+        if (count($this->allLabels) === 2) {
+            return $this->classifiers[0]->predictSampleBinary($sample);
+        }
+
+        $probs = [];
+
+        foreach ($this->classifiers as $label => $predictor) {
+            $probs[$label] = $predictor->predictProbability($sample, $label);
+        }
+
+        arsort($probs, SORT_NUMERIC);
+
+        return key($probs);
+    }
+
+    /**
+     * Each classifier should implement this method instead of train(samples, targets)
+     */
+    abstract protected function trainBinary(array $samples, array $targets, array $labels);
+
+    /**
+     * To be overwritten by OneVsRest classifiers.
+     */
+    abstract protected function resetBinary(): void;
+
+    /**
+     * Each classifier that make use of OvR approach should be able to
+     * return a probability for a sample to belong to the given label.
+     *
+     * @return mixed
+     */
+    abstract protected function predictProbability(array $sample, string $label);
+
+    /**
+     * Each classifier should implement this method instead of predictSample()
+     *
+     * @return mixed
+     */
+    abstract protected function predictSampleBinary(array $sample);
+
+    /**
+     * Groups all targets into two groups: Targets equal to
+     * the given label and the others
+     *
+     * $targets is not passed by reference nor contains objects so this method
+     * changes will not affect the caller $targets array.
+     *
+     * @param mixed $label
+     *
+     * @return array Binarized targets and target's labels
+     */
+    private function binarizeTargets(array $targets, $label): array
+    {
+        $notLabel = "not_${label}";
+        foreach ($targets as $key => $target) {
+            $targets[$key] = $target == $label ? $label : $notLabel;
+        }
+
+        $labels = [$label, $notLabel];
+
+        return [$targets, $labels];
+    }
+}