LogisticRegression::runTraining() - Code Metrics - Inspection of "Add PHPStan and level to max (#168)" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

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by Arkadiusz

created 2018-01-06 12:09 UTC

LogisticRegression::runTraining() B

↳ Parent: LogisticRegression

Complexity

Conditions	4
Paths	4

Size

Total Lines	24
Code Lines	14

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	24
rs	8.6845
c	0
b	0
f	0
cc	4
eloc	14
nc	4
nop	2

<?php

declare(strict_types=1);

namespace Phpml\Classification\Linear;

use Closure;
use Exception;
use Phpml\Helper\Optimizer\ConjugateGradient;

class LogisticRegression extends Adaline
{
    /**
     * Batch training: Gradient descent algorithm (default)
     */
    public const BATCH_TRAINING = 1;

    /**
     * Online training: Stochastic gradient descent learning
     */
    public const ONLINE_TRAINING = 2;

    /**
     * Conjugate Batch: Conjugate Gradient algorithm
     */
    public const CONJUGATE_GRAD_TRAINING = 3;

    /**
     * Cost function to optimize: 'log' and 'sse' are supported <br>
     *  - 'log' : log likelihood <br>
     *  - 'sse' : sum of squared errors <br>
     *
     * @var string
     */
    protected $costFunction = 'log';

    /**
     * Regularization term: only 'L2' is supported
     *
     * @var string
     */
    protected $penalty = 'L2';

    /**
     * Lambda (λ) parameter of regularization term. If λ is set to 0, then
     * regularization term is cancelled.
     *
     * @var float
     */
    protected $lambda = 0.5;

    /**
     * Initalize a Logistic Regression classifier with maximum number of iterations
     * and learning rule to be applied <br>
     *
     * Maximum number of iterations can be an integer value greater than 0 <br>
     * If normalizeInputs is set to true, then every input given to the algorithm will be standardized
     * by use of standard deviation and mean calculation <br>
     *
     * Cost function can be 'log' for log-likelihood and 'sse' for sum of squared errors <br>
     *
     * Penalty (Regularization term) can be 'L2' or empty string to cancel penalty term
     *
     * @throws \Exception
     */
    public function __construct(
        int $maxIterations = 500,
        bool $normalizeInputs = true,
        int $trainingType = self::CONJUGATE_GRAD_TRAINING,
        string $cost = 'log',
        string $penalty = 'L2'
    ) {
        $trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
        if (!in_array($trainingType, $trainingTypes)) {
            throw new Exception('Logistic regression can only be trained with '.
                'batch (gradient descent), online (stochastic gradient descent) '.
                'or conjugate batch (conjugate gradients) algorithms');
        }

        if (!in_array($cost, ['log', 'sse'])) {
            throw new Exception("Logistic regression cost function can be one of the following: \n".
                "'log' for log-likelihood and 'sse' for sum of squared errors");
        }

        if ($penalty != '' && strtoupper($penalty) !== 'L2') {
            throw new Exception("Logistic regression supports only 'L2' regularization");
        }

        $this->learningRate = 0.001;

        parent::__construct($this->learningRate, $maxIterations, $normalizeInputs);

        $this->trainingType = $trainingType;
        $this->costFunction = $cost;
        $this->penalty = $penalty;
    }

    /**
     * Sets the learning rate if gradient descent algorithm is
     * selected for training
     */
    public function setLearningRate(float $learningRate): void
    {
        $this->learningRate = $learningRate;
    }

    /**
     * Lambda (λ) parameter of regularization term. If 0 is given,
     * then the regularization term is cancelled
     */
    public function setLambda(float $lambda): void
    {
        $this->lambda = $lambda;
    }

    /**
     * Adapts the weights with respect to given samples and targets
     * by use of selected solver
     *
     * @throws \Exception
     */
    protected function runTraining(array $samples, array $targets): void
    {
        $callback = $this->getCostFunction();

        switch ($this->trainingType) {
            case self::BATCH_TRAINING:
                $this->runGradientDescent($samples, $targets, $callback, true);

                return;

            case self::ONLINE_TRAINING:
                $this->runGradientDescent($samples, $targets, $callback, false);

                return;

            case self::CONJUGATE_GRAD_TRAINING:
                $this->runConjugateGradient($samples, $targets, $callback);

                return;

            default:
                throw new Exception('Logistic regression has invalid training type: %s.', $this->trainingType);
        }
    }

    /**
     * Executes Conjugate Gradient method to optimize the weights of the LogReg model
     */
    protected function runConjugateGradient(array $samples, array $targets, Closure $gradientFunc): void
    {
        if ($this->optimizer === null) {
            $this->optimizer = (new ConjugateGradient($this->featureCount))
                ->setMaxIterations($this->maxIterations);
        }

        $this->weights = $this->optimizer->runOptimization($samples, $targets, $gradientFunc);
        $this->costValues = $this->optimizer->getCostValues();
    }

    /**
     * Returns the appropriate callback function for the selected cost function
     *
     * @throws \Exception
     */
    protected function getCostFunction(): Closure
    {
        $penalty = 0;
        if ($this->penalty == 'L2') {
            $penalty = $this->lambda;
        }

        switch ($this->costFunction) {
            case 'log':
                /*
                 * Negative of Log-likelihood cost function to be minimized:
                 *		J(x) = ∑( - y . log(h(x)) - (1 - y) . log(1 - h(x)))
                 *
                 * If regularization term is given, then it will be added to the cost:
                 *		for L2 : J(x) = J(x) +  λ/m . w
                 *
                 * The gradient of the cost function to be used with gradient descent:
                 *		∇J(x) = -(y - h(x)) = (h(x) - y)
                 */
                $callback = function ($weights, $sample, $y) use ($penalty) {
                    $this->weights = $weights;
                    $hX = $this->output($sample);

                    // In cases where $hX = 1 or $hX = 0, the log-likelihood
                    // value will give a NaN, so we fix these values
                    if ($hX == 1) {
                        $hX = 1 - 1e-10;
                    }

                    if ($hX == 0) {
                        $hX = 1e-10;
                    }

                    $y = $y < 0 ? 0 : 1;

                    $error = -$y * log($hX) - (1 - $y) * log(1 - $hX);
                    $gradient = $hX - $y;

                    return [$error, $gradient, $penalty];
                };

                return $callback;

            case 'sse':
                /*
                 * Sum of squared errors or least squared errors cost function:
                 *		J(x) = ∑ (y - h(x))^2
                 *
                 * If regularization term is given, then it will be added to the cost:
                 *		for L2 : J(x) = J(x) +  λ/m . w
                 *
                 * The gradient of the cost function:
                 *		∇J(x) = -(h(x) - y) . h(x) . (1 - h(x))
                 */
                $callback = function ($weights, $sample, $y) use ($penalty) {
                    $this->weights = $weights;
                    $hX = $this->output($sample);

                    $y = $y < 0 ? 0 : 1;

                    $error = ($y - $hX) ** 2;
                    $gradient = -($y - $hX) * $hX * (1 - $hX);

                    return [$error, $gradient, $penalty];
                };

                return $callback;

            default:
                throw new Exception(sprintf('Logistic regression has invalid cost function: %s.', $this->costFunction));
        }
    }

    /**
     * Returns the output of the network, a float value between 0.0 and 1.0
     */
    protected function output(array $sample): float
    {
        $sum = parent::output($sample);

        return 1.0 / (1.0 + exp(-$sum));
    }

    /**
     * Returns the class value (either -1 or 1) for the given input
     */
    protected function outputClass(array $sample): int
    {
        $output = $this->output($sample);

        if ($output > 0.5) {
            return 1;
        }

        return -1;
    }

    /**
     * Returns the probability of the sample of belonging to the given label.
     *
     * The probability is simply taken as the distance of the sample
     * to the decision plane.
     *
     * @param mixed $label
     */
    protected function predictProbability(array $sample, $label): float
    {
        $sample = $this->checkNormalizedSample($sample);
        $probability = $this->output($sample);

        if (array_search($label, $this->labels, true) > 0) {
            return $probability;
        }

        return 1 - $probability;
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Classification\Linear;
6
7			use Closure;
8			use Exception;
9			use Phpml\Helper\Optimizer\ConjugateGradient;
10
11			class LogisticRegression extends Adaline
12			{
13			/**
14			* Batch training: Gradient descent algorithm (default)
15			*/
16			public const BATCH_TRAINING = 1;
17
18			/**
19			* Online training: Stochastic gradient descent learning
20			*/
21			public const ONLINE_TRAINING = 2;
22
23			/**
24			* Conjugate Batch: Conjugate Gradient algorithm
25			*/
26			public const CONJUGATE_GRAD_TRAINING = 3;
27
28			/**
29			* Cost function to optimize: 'log' and 'sse' are supported <br>
30			* - 'log' : log likelihood <br>
31			* - 'sse' : sum of squared errors <br>
32			*
33			* @var string
34			*/
35			protected $costFunction = 'log';
36
37			/**
38			* Regularization term: only 'L2' is supported
39			*
40			* @var string
41			*/
42			protected $penalty = 'L2';
43
44			/**
45			* Lambda (λ) parameter of regularization term. If λ is set to 0, then
46			* regularization term is cancelled.
47			*
48			* @var float
49			*/
50			protected $lambda = 0.5;
51
52			/**
53			* Initalize a Logistic Regression classifier with maximum number of iterations
54			* and learning rule to be applied <br>
55			*
56			* Maximum number of iterations can be an integer value greater than 0 <br>
57			* If normalizeInputs is set to true, then every input given to the algorithm will be standardized
58			* by use of standard deviation and mean calculation <br>
59			*
60			* Cost function can be 'log' for log-likelihood and 'sse' for sum of squared errors <br>
61			*
62			* Penalty (Regularization term) can be 'L2' or empty string to cancel penalty term
63			*
64			* @throws \Exception
65			*/
66			public function __construct(
67			int $maxIterations = 500,
68			bool $normalizeInputs = true,
69			int $trainingType = self::CONJUGATE_GRAD_TRAINING,
70			string $cost = 'log',
71			string $penalty = 'L2'
72			) {
73			$trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
74			if (!in_array($trainingType, $trainingTypes)) {
75			throw new Exception('Logistic regression can only be trained with '.
76			'batch (gradient descent), online (stochastic gradient descent) '.
77			'or conjugate batch (conjugate gradients) algorithms');
78			}
79
80			if (!in_array($cost, ['log', 'sse'])) {
81			throw new Exception("Logistic regression cost function can be one of the following: \n".
82			"'log' for log-likelihood and 'sse' for sum of squared errors");
83			}
84
85			if ($penalty != '' && strtoupper($penalty) !== 'L2') {
86			throw new Exception("Logistic regression supports only 'L2' regularization");
87			}
88
89			$this->learningRate = 0.001;
90
91			parent::__construct($this->learningRate, $maxIterations, $normalizeInputs);
92
93			$this->trainingType = $trainingType;
94			$this->costFunction = $cost;
95			$this->penalty = $penalty;
96			}
97
98			/**
99			* Sets the learning rate if gradient descent algorithm is
100			* selected for training
101			*/
102			public function setLearningRate(float $learningRate): void
103			{
104			$this->learningRate = $learningRate;
105			}
106
107			/**
108			* Lambda (λ) parameter of regularization term. If 0 is given,
109			* then the regularization term is cancelled
110			*/
111			public function setLambda(float $lambda): void
112			{
113			$this->lambda = $lambda;
114			}
115
116			/**
117			* Adapts the weights with respect to given samples and targets
118			* by use of selected solver
119			*
120			* @throws \Exception
121			*/
122			protected function runTraining(array $samples, array $targets): void
123			{
124			$callback = $this->getCostFunction();
125
126			switch ($this->trainingType) {
127			case self::BATCH_TRAINING:
128			$this->runGradientDescent($samples, $targets, $callback, true);
129
130			return;
131
132			case self::ONLINE_TRAINING:
133			$this->runGradientDescent($samples, $targets, $callback, false);
134
135			return;
136
137			case self::CONJUGATE_GRAD_TRAINING:
138			$this->runConjugateGradient($samples, $targets, $callback);
139
140			return;
141
142			default:
143			throw new Exception('Logistic regression has invalid training type: %s.', $this->trainingType);
144			}
145			}
146
147			/**
148			* Executes Conjugate Gradient method to optimize the weights of the LogReg model
149			*/
150			protected function runConjugateGradient(array $samples, array $targets, Closure $gradientFunc): void
151			{
152			if ($this->optimizer === null) {
153			$this->optimizer = (new ConjugateGradient($this->featureCount))
154			->setMaxIterations($this->maxIterations);
155			}
156
157			$this->weights = $this->optimizer->runOptimization($samples, $targets, $gradientFunc);
158			$this->costValues = $this->optimizer->getCostValues();
159			}
160
161			/**
162			* Returns the appropriate callback function for the selected cost function
163			*
164			* @throws \Exception
165			*/
166			protected function getCostFunction(): Closure
167			{
168			$penalty = 0;
169			if ($this->penalty == 'L2') {
170			$penalty = $this->lambda;
171			}
172
173			switch ($this->costFunction) {
174			case 'log':
175			/*
176			* Negative of Log-likelihood cost function to be minimized:
177			* J(x) = ∑( - y . log(h(x)) - (1 - y) . log(1 - h(x)))
178			*
179			* If regularization term is given, then it will be added to the cost:
180			* for L2 : J(x) = J(x) + λ/m . w
181			*
182			* The gradient of the cost function to be used with gradient descent:
183			* ∇J(x) = -(y - h(x)) = (h(x) - y)
184			*/
185			$callback = function ($weights, $sample, $y) use ($penalty) {
186			$this->weights = $weights;
187			$hX = $this->output($sample);
188
189			// In cases where $hX = 1 or $hX = 0, the log-likelihood
190			// value will give a NaN, so we fix these values
191			if ($hX == 1) {
192			$hX = 1 - 1e-10;
193			}
194
195			if ($hX == 0) {
196			$hX = 1e-10;
197			}
198
199			$y = $y < 0 ? 0 : 1;
200
201			$error = -$y * log($hX) - (1 - $y) * log(1 - $hX);
202			$gradient = $hX - $y;
203
204			return [$error, $gradient, $penalty];
205			};
206
207			return $callback;
208
209			case 'sse':
210			/*
211			* Sum of squared errors or least squared errors cost function:
212			* J(x) = ∑ (y - h(x))^2
213			*
214			* If regularization term is given, then it will be added to the cost:
215			* for L2 : J(x) = J(x) + λ/m . w
216			*
217			* The gradient of the cost function:
218			* ∇J(x) = -(h(x) - y) . h(x) . (1 - h(x))
219			*/
220			$callback = function ($weights, $sample, $y) use ($penalty) {
221			$this->weights = $weights;
222			$hX = $this->output($sample);
223
224			$y = $y < 0 ? 0 : 1;
225
226			$error = ($y - $hX) ** 2;
227			$gradient = -($y - $hX) * $hX * (1 - $hX);
228
229			return [$error, $gradient, $penalty];
230			};
231
232			return $callback;
233
234			default:
235			throw new Exception(sprintf('Logistic regression has invalid cost function: %s.', $this->costFunction));
236			}
237			}
238
239			/**
240			* Returns the output of the network, a float value between 0.0 and 1.0
241			*/
242			protected function output(array $sample): float
243			{
244			$sum = parent::output($sample);
245
246			return 1.0 / (1.0 + exp(-$sum));
247			}
248
249			/**
250			* Returns the class value (either -1 or 1) for the given input
251			*/
252			protected function outputClass(array $sample): int
253			{
254			$output = $this->output($sample);
255
256			if ($output > 0.5) {
257			return 1;
258			}
259
260			return -1;
261			}
262
263			/**
264			* Returns the probability of the sample of belonging to the given label.
265			*
266			* The probability is simply taken as the distance of the sample
267			* to the decision plane.
268			*
269			* @param mixed $label
270			*/
271			protected function predictProbability(array $sample, $label): float
272			{
273			$sample = $this->checkNormalizedSample($sample);
274			$probability = $this->output($sample);
275
276			if (array_search($label, $this->labels, true) > 0) {
277			return $probability;
278			}
279
280			return 1 - $probability;
281			}
282			}
283

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LogisticRegression::runTraining() B

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