AdaBoost::train() - Code Metrics - Inspection of "Implement OneHotEncoder (#384)" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Branch — master (4590d5)

by Arkadiusz

created 2019-05-15 06:06 UTC

AdaBoost::train() A

↳ Parent: AdaBoost

Complexity

Conditions	5
Paths	7

Size

Total Lines	39
Code Lines	22

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	22
dl	0
loc	39
rs	9.2568
c	0
b	0
f	0
cc	5
nc	7
nop	2

<?php

declare(strict_types=1);

namespace Phpml\Classification\Ensemble;

use Phpml\Classification\Classifier;
use Phpml\Classification\Linear\DecisionStump;
use Phpml\Classification\WeightedClassifier;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
use Phpml\Math\Statistic\Mean;
use Phpml\Math\Statistic\StandardDeviation;
use ReflectionClass;

class AdaBoost implements Classifier
{
    use Predictable;
    use Trainable;

    /**
     * Actual labels given in the targets array
     *
     * @var array
     */
    protected $labels = [];

    /**
     * @var int
     */
    protected $sampleCount;

    /**
     * @var int
     */
    protected $featureCount;

    /**
     * Number of maximum iterations to be done
     *
     * @var int
     */
    protected $maxIterations;

    /**
     * Sample weights
     *
     * @var array
     */
    protected $weights = [];

    /**
     * List of selected 'weak' classifiers
     *
     * @var array
     */
    protected $classifiers = [];

    /**
     * Base classifier weights
     *
     * @var array
     */
    protected $alpha = [];

    /**
     * @var string
     */
    protected $baseClassifier = DecisionStump::class;

    /**
     * @var array
     */
    protected $classifierOptions = [];

    /**
     * ADAptive BOOSTing (AdaBoost) is an ensemble algorithm to
     * improve classification performance of 'weak' classifiers such as
     * DecisionStump (default base classifier of AdaBoost).
     */
    public function __construct(int $maxIterations = 50)
    {
        $this->maxIterations = $maxIterations;
    }

    /**
     * Sets the base classifier that will be used for boosting (default = DecisionStump)
     */
    public function setBaseClassifier(string $baseClassifier = DecisionStump::class, array $classifierOptions = []): void
    {
        $this->baseClassifier = $baseClassifier;
        $this->classifierOptions = $classifierOptions;
    }

    /**
     * @throws InvalidArgumentException
     */
    public function train(array $samples, array $targets): void
    {
        // Initialize usual variables
        $this->labels = array_keys(array_count_values($targets));
        if (count($this->labels) !== 2) {
            throw new InvalidArgumentException('AdaBoost is a binary classifier and can classify between two classes only');
        }

        // Set all target values to either -1 or 1
        $this->labels = [
            1 => $this->labels[0],
            -1 => $this->labels[1],
        ];
        foreach ($targets as $target) {
            $this->targets[] = $target == $this->labels[1] ? 1 : -1;
        }

        $this->samples = array_merge($this->samples, $samples);
        $this->featureCount = count($samples[0]);
        $this->sampleCount = count($this->samples);

        // Initialize AdaBoost parameters
        $this->weights = array_fill(0, $this->sampleCount, 1.0 / $this->sampleCount);
        $this->classifiers = [];
        $this->alpha = [];

        // Execute the algorithm for a maximum number of iterations
        $currIter = 0;
        while ($this->maxIterations > $currIter++) {
            // Determine the best 'weak' classifier based on current weights
            $classifier = $this->getBestClassifier();
            $errorRate = $this->evaluateClassifier($classifier);

            // Update alpha & weight values at each iteration
            $alpha = $this->calculateAlpha($errorRate);
            $this->updateWeights($classifier, $alpha);

            $this->classifiers[] = $classifier;
            $this->alpha[] = $alpha;
        }
    }

    /**
     * @return mixed
     */
    public function predictSample(array $sample)
    {
        $sum = 0;
        foreach ($this->alpha as $index => $alpha) {
            $h = $this->classifiers[$index]->predict($sample);
            $sum += $h * $alpha;
        }

        return $this->labels[$sum > 0 ? 1 : -1];
    }

    /**
     * Returns the classifier with the lowest error rate with the
     * consideration of current sample weights
     */
    protected function getBestClassifier(): Classifier
    {
        $ref = new ReflectionClass($this->baseClassifier);
        /** @var Classifier $classifier */
        $classifier = count($this->classifierOptions) === 0 ? $ref->newInstance() : $ref->newInstanceArgs($this->classifierOptions);

        if ($classifier instanceof WeightedClassifier) {
            $classifier->setSampleWeights($this->weights);
            $classifier->train($this->samples, $this->targets);
        } else {
            [$samples, $targets] = $this->resample();
            $classifier->train($samples, $targets);
        }

        return $classifier;
    }

    /**
     * Resamples the dataset in accordance with the weights and
     * returns the new dataset
     */
    protected function resample(): array
    {
        $weights = $this->weights;
        $std = StandardDeviation::population($weights);
        $mean = Mean::arithmetic($weights);
        $min = min($weights);
        $minZ = (int) round(($min - $mean) / $std);

        $samples = [];
        $targets = [];
        foreach ($weights as $index => $weight) {
            $z = (int) round(($weight - $mean) / $std) - $minZ + 1;
            for ($i = 0; $i < $z; ++$i) {
                if (random_int(0, 1) == 0) {
                    continue;
                }

                $samples[] = $this->samples[$index];
                $targets[] = $this->targets[$index];
            }
        }

        return [$samples, $targets];
    }

    /**
     * Evaluates the classifier and returns the classification error rate
     */
    protected function evaluateClassifier(Classifier $classifier): float
    {
        $total = (float) array_sum($this->weights);
        $wrong = 0;
        foreach ($this->samples as $index => $sample) {
            $predicted = $classifier->predict($sample);
            if ($predicted != $this->targets[$index]) {
                $wrong += $this->weights[$index];
            }
        }

        return $wrong / $total;
    }

    /**
     * Calculates alpha of a classifier
     */
    protected function calculateAlpha(float $errorRate): float
    {
        if ($errorRate == 0) {
            $errorRate = 1e-10;
        }

        return 0.5 * log((1 - $errorRate) / $errorRate);
    }

    /**
     * Updates the sample weights
     */
    protected function updateWeights(Classifier $classifier, float $alpha): void
    {
        $sumOfWeights = array_sum($this->weights);
        $weightsT1 = [];
        foreach ($this->weights as $index => $weight) {
            $desired = $this->targets[$index];
            $output = $classifier->predict($this->samples[$index]);

            $weight *= exp(-$alpha * $desired * $output) / $sumOfWeights;

            $weightsT1[] = $weight;
        }

        $this->weights = $weightsT1;
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Classification\Ensemble;
6
7			use Phpml\Classification\Classifier;
8			use Phpml\Classification\Linear\DecisionStump;
9			use Phpml\Classification\WeightedClassifier;
10			use Phpml\Exception\InvalidArgumentException;
11			use Phpml\Helper\Predictable;
12			use Phpml\Helper\Trainable;
13			use Phpml\Math\Statistic\Mean;
14			use Phpml\Math\Statistic\StandardDeviation;
15			use ReflectionClass;
16
17			class AdaBoost implements Classifier
18			{
19			use Predictable;
20			use Trainable;
21
22			/**
23			* Actual labels given in the targets array
24			*
25			* @var array
26			*/
27			protected $labels = [];
28
29			/**
30			* @var int
31			*/
32			protected $sampleCount;
33
34			/**
35			* @var int
36			*/
37			protected $featureCount;
38
39			/**
40			* Number of maximum iterations to be done
41			*
42			* @var int
43			*/
44			protected $maxIterations;
45
46			/**
47			* Sample weights
48			*
49			* @var array
50			*/
51			protected $weights = [];
52
53			/**
54			* List of selected 'weak' classifiers
55			*
56			* @var array
57			*/
58			protected $classifiers = [];
59
60			/**
61			* Base classifier weights
62			*
63			* @var array
64			*/
65			protected $alpha = [];
66
67			/**
68			* @var string
69			*/
70			protected $baseClassifier = DecisionStump::class;
71
72			/**
73			* @var array
74			*/
75			protected $classifierOptions = [];
76
77			/**
78			* ADAptive BOOSTing (AdaBoost) is an ensemble algorithm to
79			* improve classification performance of 'weak' classifiers such as
80			* DecisionStump (default base classifier of AdaBoost).
81			*/
82			public function __construct(int $maxIterations = 50)
83			{
84			$this->maxIterations = $maxIterations;
85			}
86
87			/**
88			* Sets the base classifier that will be used for boosting (default = DecisionStump)
89			*/
90			public function setBaseClassifier(string $baseClassifier = DecisionStump::class, array $classifierOptions = []): void
91			{
92			$this->baseClassifier = $baseClassifier;
93			$this->classifierOptions = $classifierOptions;
94			}
95
96			/**
97			* @throws InvalidArgumentException
98			*/
99			public function train(array $samples, array $targets): void
100			{
101			// Initialize usual variables
102			$this->labels = array_keys(array_count_values($targets));
103			if (count($this->labels) !== 2) {
104			throw new InvalidArgumentException('AdaBoost is a binary classifier and can classify between two classes only');
105			}
106
107			// Set all target values to either -1 or 1
108			$this->labels = [
109			1 => $this->labels[0],
110			-1 => $this->labels[1],
111			];
112			foreach ($targets as $target) {
113			$this->targets[] = $target == $this->labels[1] ? 1 : -1;
114			}
115
116			$this->samples = array_merge($this->samples, $samples);
117			$this->featureCount = count($samples[0]);
118			$this->sampleCount = count($this->samples);
119
120			// Initialize AdaBoost parameters
121			$this->weights = array_fill(0, $this->sampleCount, 1.0 / $this->sampleCount);
122			$this->classifiers = [];
123			$this->alpha = [];
124
125			// Execute the algorithm for a maximum number of iterations
126			$currIter = 0;
127			while ($this->maxIterations > $currIter++) {
128			// Determine the best 'weak' classifier based on current weights
129			$classifier = $this->getBestClassifier();
130			$errorRate = $this->evaluateClassifier($classifier);
131
132			// Update alpha & weight values at each iteration
133			$alpha = $this->calculateAlpha($errorRate);
134			$this->updateWeights($classifier, $alpha);
135
136			$this->classifiers[] = $classifier;
137			$this->alpha[] = $alpha;
138			}
139			}
140
141			/**
142			* @return mixed
143			*/
144			public function predictSample(array $sample)
145			{
146			$sum = 0;
147			foreach ($this->alpha as $index => $alpha) {
148			$h = $this->classifiers[$index]->predict($sample);
149			$sum += $h * $alpha;
150			}
151
152			return $this->labels[$sum > 0 ? 1 : -1];
153			}
154
155			/**
156			* Returns the classifier with the lowest error rate with the
157			* consideration of current sample weights
158			*/
159			protected function getBestClassifier(): Classifier
160			{
161			$ref = new ReflectionClass($this->baseClassifier);
162			/** @var Classifier $classifier */
163			$classifier = count($this->classifierOptions) === 0 ? $ref->newInstance() : $ref->newInstanceArgs($this->classifierOptions);
164
165			if ($classifier instanceof WeightedClassifier) {
166			$classifier->setSampleWeights($this->weights);
167			$classifier->train($this->samples, $this->targets);
168			} else {
169			[$samples, $targets] = $this->resample();
170			$classifier->train($samples, $targets);
171			}
172
173			return $classifier;
174			}
175
176			/**
177			* Resamples the dataset in accordance with the weights and
178			* returns the new dataset
179			*/
180			protected function resample(): array
181			{
182			$weights = $this->weights;
183			$std = StandardDeviation::population($weights);
184			$mean = Mean::arithmetic($weights);
185			$min = min($weights);
186			$minZ = (int) round(($min - $mean) / $std);
187
188			$samples = [];
189			$targets = [];
190			foreach ($weights as $index => $weight) {
191			$z = (int) round(($weight - $mean) / $std) - $minZ + 1;
192			for ($i = 0; $i < $z; ++$i) {
193			if (random_int(0, 1) == 0) {
194			continue;
195			}
196
197			$samples[] = $this->samples[$index];
198			$targets[] = $this->targets[$index];
199			}
200			}
201
202			return [$samples, $targets];
203			}
204
205			/**
206			* Evaluates the classifier and returns the classification error rate
207			*/
208			protected function evaluateClassifier(Classifier $classifier): float
209			{
210			$total = (float) array_sum($this->weights);
211			$wrong = 0;
212			foreach ($this->samples as $index => $sample) {
213			$predicted = $classifier->predict($sample);
214			if ($predicted != $this->targets[$index]) {
215			$wrong += $this->weights[$index];
216			}
217			}
218
219			return $wrong / $total;
220			}
221
222			/**
223			* Calculates alpha of a classifier
224			*/
225			protected function calculateAlpha(float $errorRate): float
226			{
227			if ($errorRate == 0) {
228			$errorRate = 1e-10;
229			}
230
231			return 0.5 * log((1 - $errorRate) / $errorRate);
232			}
233
234			/**
235			* Updates the sample weights
236			*/
237			protected function updateWeights(Classifier $classifier, float $alpha): void
238			{
239			$sumOfWeights = array_sum($this->weights);
240			$weightsT1 = [];
241			foreach ($this->weights as $index => $weight) {
242			$desired = $this->targets[$index];
243			$output = $classifier->predict($this->samples[$index]);
244
245			$weight = exp(-$alpha $desired * $output) / $sumOfWeights;
246
247			$weightsT1[] = $weight;
248			}
249
250			$this->weights = $weightsT1;
251			}
252			}
253

php-ai / php-ml

Branch — master (4590d5)

AdaBoost::train() A

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