OneVsRest::trainByLabel() - Code Metrics - Inspection of "Partial training base (#78)" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

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by Arkadiusz

created 2017-04-19 20:26 UTC

OneVsRest::trainByLabel() C

↳ Parent: OneVsRest

Complexity

Conditions	7
Paths	12

Size

Total Lines	43
Code Lines	19

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	43
rs	6.7272
c	0
b	0
f	0
cc	7
eloc	19
nc	12
nop	3

<?php

declare(strict_types=1);

namespace Phpml\Helper;

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
     *
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets)
    {
        // Clears previous stuff.
        $this->reset();

        return $this->trainBylabel($samples, $targets);
    }

    /**
     * @param array $samples
     * @param array $targets
     * @param array $allLabels All training set labels
     * @return void
     */
    protected function trainByLabel(array $samples, array $targets, array $allLabels = array())
    {

        // Overwrites the current value if it exist. $allLabels must be provided for each partialTrain run.
        if (!empty($allLabels)) {
            $this->allLabels = $allLabels;
        } else {
            $this->allLabels = array_keys(array_count_values($targets));
        }
        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 (empty($this->classifiers)) {
                $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 (empty($this->classifiers[$label])) {
                    $this->classifiers[$label] = $this->getClassifierCopy();
                }

                list($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();
        }
    }

    /**
     * Resets the classifier and the vars internally used by OneVsRest to create multiple classifiers.
     */
    public function reset()
    {
        $this->classifiers = [];
        $this->allLabels = [];
        $this->costValues = [];

        $this->resetBinary();
    }

    /**
     * Returns an instance of the current class after cleaning up OneVsRest stuff.
     *
     * @return \Phpml\Estimator
     */
    protected function getClassifierCopy()
    {

        // 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;
    }

    /**
     * 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 array $targets
     * @param mixed $label
     * @return array Binarized targets and target's labels
     */
    private function binarizeTargets($targets, $label)
    {

        $notLabel = "not_$label";
        foreach ($targets as $key => $target) {
            $targets[$key] = $target == $label ? $label : $notLabel;
        }

        $labels = array($label, $notLabel);
        return array($targets, $labels);
    }


    /**
     * @param array $sample
     *
     * @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)
     *
     * @param array $samples
     * @param array $targets
     * @param array $labels
     */
    abstract protected function trainBinary(array $samples, array $targets, array $labels);

    /**
     * To be overwritten by OneVsRest classifiers.
     *
     * @return void
     */
    abstract protected function resetBinary();

    /**
     * Each classifier that make use of OvR approach should be able to
     * return a probability for a sample to belong to the given label.
     *
     * @param array $sample
     *
     * @return mixed
     */
    abstract protected function predictProbability(array $sample, string $label);

    /**
     * Each classifier should implement this method instead of predictSample()
     *
     * @param array $sample
     *
     * @return mixed
     */
    abstract protected function predictSampleBinary(array $sample);
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Helper;
6
7			trait OneVsRest
8			{
9
10			/**
11			* @var array
12			*/
13			protected $classifiers = [];
14
15			/**
16			* All provided training targets' labels.
17			*
18			* @var array
19			*/
20			protected $allLabels = [];
21
22			/**
23			* @var array
24			*/
25			protected $costValues = [];
26
27			/**
28			* Train a binary classifier in the OvR style
29			*
30			* @param array $samples
31			* @param array $targets
32			*/
33			public function train(array $samples, array $targets)
34			{
35			// Clears previous stuff.
36			$this->reset();
37
38			return $this->trainBylabel($samples, $targets);
39			}
40
41			/**
42			* @param array $samples
43			* @param array $targets
44			* @param array $allLabels All training set labels
45			* @return void
46			*/
47			protected function trainByLabel(array $samples, array $targets, array $allLabels = array())
48			{
49
50			// Overwrites the current value if it exist. $allLabels must be provided for each partialTrain run.
51			if (!empty($allLabels)) {
52			$this->allLabels = $allLabels;
53			} else {
54			$this->allLabels = array_keys(array_count_values($targets));
55			}
56			sort($this->allLabels, SORT_STRING);
57
58			// If there are only two targets, then there is no need to perform OvR
59			if (count($this->allLabels) == 2) {
60
61			// Init classifier if required.
62			if (empty($this->classifiers)) {
63			$this->classifiers[0] = $this->getClassifierCopy();
64			}
65
66			$this->classifiers[0]->trainBinary($samples, $targets, $this->allLabels);
67			} else {
68			// Train a separate classifier for each label and memorize them
69
70			foreach ($this->allLabels as $label) {
71
72			// Init classifier if required.
73			if (empty($this->classifiers[$label])) {
74			$this->classifiers[$label] = $this->getClassifierCopy();
75			}
76
77			list($binarizedTargets, $classifierLabels) = $this->binarizeTargets($targets, $label);
78			$this->classifiers[$label]->trainBinary($samples, $binarizedTargets, $classifierLabels);
79			}
80			}
81
82			// If the underlying classifier is capable of giving the cost values
83			// during the training, then assign it to the relevant variable
84			// Adding just the first classifier cost values to avoid complex average calculations.
85			$classifierref = reset($this->classifiers);
86			if (method_exists($classifierref, 'getCostValues')) {
87			$this->costValues = $classifierref->getCostValues();
88			}
89			}
90
91			/**
92			* Resets the classifier and the vars internally used by OneVsRest to create multiple classifiers.
93			*/
94			public function reset()
95			{
96			$this->classifiers = [];
97			$this->allLabels = [];
98			$this->costValues = [];
99
100			$this->resetBinary();
101			}
102
103			/**
104			* Returns an instance of the current class after cleaning up OneVsRest stuff.
105			*
106			* @return \Phpml\Estimator
107			*/
108			protected function getClassifierCopy()
109			{
110
111			// Clone the current classifier, so that
112			// we don't mess up its variables while training
113			// multiple instances of this classifier
114			$classifier = clone $this;
115			$classifier->reset();
116			return $classifier;
117			}
118
119			/**
120			* Groups all targets into two groups: Targets equal to
121			* the given label and the others
122			*
123			* $targets is not passed by reference nor contains objects so this method
124			* changes will not affect the caller $targets array.
125			*
126			* @param array $targets
127			* @param mixed $label
128			* @return array Binarized targets and target's labels
129			*/
130			private function binarizeTargets($targets, $label)
131			{
132
133			$notLabel = "not_$label";
134			foreach ($targets as $key => $target) {
135			$targets[$key] = $target == $label ? $label : $notLabel;
136			}
137
138			$labels = array($label, $notLabel);
139			return array($targets, $labels);
140			}
141
142
143			/**
144			* @param array $sample
145			*
146			* @return mixed
147			*/
148			protected function predictSample(array $sample)
149			{
150			if (count($this->allLabels) == 2) {
151			return $this->classifiers[0]->predictSampleBinary($sample);
152			}
153
154			$probs = [];
155
156			foreach ($this->classifiers as $label => $predictor) {
157			$probs[$label] = $predictor->predictProbability($sample, $label);
158			}
159
160			arsort($probs, SORT_NUMERIC);
161			return key($probs);
162			}
163
164			/**
165			* Each classifier should implement this method instead of train(samples, targets)
166			*
167			* @param array $samples
168			* @param array $targets
169			* @param array $labels
170			*/
171			abstract protected function trainBinary(array $samples, array $targets, array $labels);
172
173			/**
174			* To be overwritten by OneVsRest classifiers.
175			*
176			* @return void
177			*/
178			abstract protected function resetBinary();
179
180			/**
181			* Each classifier that make use of OvR approach should be able to
182			* return a probability for a sample to belong to the given label.
183			*
184			* @param array $sample
185			*
186			* @return mixed
187			*/
188			abstract protected function predictProbability(array $sample, string $label);
189
190			/**
191			* Each classifier should implement this method instead of predictSample()
192			*
193			* @param array $sample
194			*
195			* @return mixed
196			*/
197			abstract protected function predictSampleBinary(array $sample);
198			}
199

php-ai / php-ml

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OneVsRest::trainByLabel() C

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