NaiveBayes::calculateStatistics() - Code Metrics - Inspection of "Ensemble Classifiers : Bagging and RandomForest" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#36)

unknown

created 2017-02-02 12:43 UTC

NaiveBayes::calculateStatistics() B

↳ Parent: Project

Complexity

Conditions	3
Paths	3

Size

Total Lines	27
Code Lines	18

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	27
rs	8.8571
c	0
b	0
f	0
cc	3
eloc	18
nc	3
nop	2

<?php

declare(strict_types=1);

namespace Phpml\Classification;

use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
use Phpml\Math\Statistic\Mean;
use Phpml\Math\Statistic\StandardDeviation;

class NaiveBayes implements Classifier

{
    use Trainable, Predictable;

    const CONTINUOS    = 1;
    const NOMINAL    = 2;
    const EPSILON = 1e-10;

    /**
     * @var array
     */
    private $std = [];

    /**
     * @var array
     */
    private $mean= [];

    /**
     * @var array
     */
    private $discreteProb = [];

    /**
     * @var array
     */
    private $dataType = [];

    /**
     * @var array
     */
    private $p = [];

    /**
     * @var int
     */
    private $sampleCount = 0;

    /**
     * @var int
     */
    private $featureCount = 0;

    /**
     * @var array
     */
    private $labels = [];

    /**
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets)
    {
        $this->samples = array_merge($this->samples, $samples);
        $this->targets = array_merge($this->targets, $targets);
        $this->sampleCount = count($this->samples);
        $this->featureCount = count($this->samples[0]);

<<<<<<< HEAD

        $labelCounts = array_count_values($targets);
=======
        $labelCounts = array_count_values($this->targets);
>>>>>>> refs/remotes/php-ai/master
        $this->labels = array_keys($labelCounts);
        foreach ($this->labels as $label) {
            $samples = $this->getSamplesByLabel($label);
            $this->p[$label] = count($samples) / $this->sampleCount;
            $this->calculateStatistics($label, $samples);
        }
    }

    /**
     * Calculates vital statistics for each label & feature. Stores these
     * values in private array in order to avoid repeated calculation
     * @param string $label
     * @param array $samples
     */
    private function calculateStatistics($label, $samples)
    {
        $this->std[$label] = array_fill(0, $this->featureCount, 0);
        $this->mean[$label]= array_fill(0, $this->featureCount, 0);
        $this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
        $this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
        for ($i=0; $i<$this->featureCount; $i++) {
            // Get the values of nth column in the samples array
            // Mean::arithmetic is called twice, can be optimized
            $values = array_column($samples, $i);
            $numValues = count($values);
            // if the values contain non-numeric data,
            // then it should be treated as nominal/categorical/discrete column
            if ($values !== array_filter($values, 'is_numeric')) {
                $this->dataType[$label][$i] = self::NOMINAL;
                $this->discreteProb[$label][$i] = array_count_values($values);
                $db = &$this->discreteProb[$label][$i];
                $db = array_map(function ($el) use ($numValues) {
                    return $el / $numValues;
                }, $db);
            } else {
                $this->mean[$label][$i] = Mean::arithmetic($values);
                // Add epsilon in order to avoid zero stdev
                $this->std[$label][$i] = 1e-10 + StandardDeviation::population($values, false);
            }
        }
    }

    /**
     * Calculates the probability P(label|sample_n)
     *
     * @param array $sample
     * @param int $feature
     * @param string $label
     * @return float
     */
    private function sampleProbability($sample, $feature, $label)
    {
        $value = $sample[$feature];
        if ($this->dataType[$label][$feature] == self::NOMINAL) {
            if (! isset($this->discreteProb[$label][$feature][$value]) ||
                $this->discreteProb[$label][$feature][$value] == 0) {
                return self::EPSILON;
            }
            return $this->discreteProb[$label][$feature][$value];
        }
        $std = $this->std[$label][$feature] ;
        $mean= $this->mean[$label][$feature];
        // Calculate the probability density by use of normal/Gaussian distribution
        // Ref: https://en.wikipedia.org/wiki/Normal_distribution
        //
        // In order to avoid numerical errors because of small or zero values,
        // some libraries adopt taking log of calculations such as
        // scikit-learn did.
        // (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
        $pdf  =  -0.5 * log(2.0 * pi() * $std * $std);
        $pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
        return $pdf;
    }

    /**
     * Return samples belonging to specific label
     * @param string $label
     * @return array
     */
    private function getSamplesByLabel($label)
    {
        $samples = [];
        for ($i=0; $i<$this->sampleCount; $i++) {
            if ($this->targets[$i] == $label) {
                $samples[] = $this->samples[$i];
            }
        }
        return $samples;
    }

    /**
     * @param array $sample
     * @return mixed
     */
    protected function predictSample(array $sample)
    {
        // Use NaiveBayes assumption for each label using:
        //	P(label|features) = P(label) * P(feature0|label) * P(feature1|label) .... P(featureN|label)
        // Then compare probability for each class to determine which label is most likely
<<<<<<< HEAD
        $predictions = array();
=======
        $predictions = [];
>>>>>>> refs/remotes/php-ai/master
        foreach ($this->labels as $label) {
            $p = $this->p[$label];
            for ($i=0; $i<$this->featureCount; $i++) {
                $Plf = $this->sampleProbability($sample, $i, $label);
                $p += $Plf;
            }
            $predictions[$label] = $p;
        }
        arsort($predictions, SORT_NUMERIC);
        reset($predictions);
        return key($predictions);
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Classification;
6
7			use Phpml\Helper\Predictable;
8			use Phpml\Helper\Trainable;
9			use Phpml\Math\Statistic\Mean;
10			use Phpml\Math\Statistic\StandardDeviation;
11
12			class NaiveBayes implements Classifier
			0 ignored issues – show Bug introduced 2017-02-02 12:46 UTC by Report Bug Copy Issue Report Possible parse error: class missing opening or closing brace Loading history...
13			{
14			use Trainable, Predictable;
15
16			const CONTINUOS = 1;
17			const NOMINAL = 2;
18			const EPSILON = 1e-10;
19
20			/**
21			* @var array
22			*/
23			private $std = [];
24
25			/**
26			* @var array
27			*/
28			private $mean= [];
29
30			/**
31			* @var array
32			*/
33			private $discreteProb = [];
34
35			/**
36			* @var array
37			*/
38			private $dataType = [];
39
40			/**
41			* @var array
42			*/
43			private $p = [];
44
45			/**
46			* @var int
47			*/
48			private $sampleCount = 0;
49
50			/**
51			* @var int
52			*/
53			private $featureCount = 0;
54
55			/**
56			* @var array
57			*/
58			private $labels = [];
59
60			/**
61			* @param array $samples
62			* @param array $targets
63			*/
64			public function train(array $samples, array $targets)
65			{
66			$this->samples = array_merge($this->samples, $samples);
67			$this->targets = array_merge($this->targets, $targets);
68			$this->sampleCount = count($this->samples);
69			$this->featureCount = count($this->samples[0]);
70
71			<<<<<<< HEAD
			0 ignored issues – show Bug introduced 2017-02-02 12:46 UTC by Report Bug Copy Issue Report This code did not parse for me. Apparently, there is an error somewhere around this line: Syntax error, unexpected T_SL Loading history...
72			$labelCounts = array_count_values($targets);
73			=======
74			$labelCounts = array_count_values($this->targets);
75			>>>>>>> refs/remotes/php-ai/master
76			$this->labels = array_keys($labelCounts);
77			foreach ($this->labels as $label) {
78			$samples = $this->getSamplesByLabel($label);
79			$this->p[$label] = count($samples) / $this->sampleCount;
80			$this->calculateStatistics($label, $samples);
81			}
82			}
83
84			/**
85			* Calculates vital statistics for each label & feature. Stores these
86			* values in private array in order to avoid repeated calculation
87			* @param string $label
88			* @param array $samples
89			*/
90			private function calculateStatistics($label, $samples)
91			{
92			$this->std[$label] = array_fill(0, $this->featureCount, 0);
93			$this->mean[$label]= array_fill(0, $this->featureCount, 0);
94			$this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
95			$this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
96			for ($i=0; $i<$this->featureCount; $i++) {
97			// Get the values of nth column in the samples array
98			// Mean::arithmetic is called twice, can be optimized
99			$values = array_column($samples, $i);
100			$numValues = count($values);
101			// if the values contain non-numeric data,
102			// then it should be treated as nominal/categorical/discrete column
103			if ($values !== array_filter($values, 'is_numeric')) {
104			$this->dataType[$label][$i] = self::NOMINAL;
105			$this->discreteProb[$label][$i] = array_count_values($values);
106			$db = &$this->discreteProb[$label][$i];
107			$db = array_map(function ($el) use ($numValues) {
108			return $el / $numValues;
109			}, $db);
110			} else {
111			$this->mean[$label][$i] = Mean::arithmetic($values);
112			// Add epsilon in order to avoid zero stdev
113			$this->std[$label][$i] = 1e-10 + StandardDeviation::population($values, false);
114			}
115			}
116			}
117
118			/**
119			* Calculates the probability P(label\|sample_n)
120			*
121			* @param array $sample
122			* @param int $feature
123			* @param string $label
124			* @return float
125			*/
126			private function sampleProbability($sample, $feature, $label)
127			{
128			$value = $sample[$feature];
129			if ($this->dataType[$label][$feature] == self::NOMINAL) {
130			if (! isset($this->discreteProb[$label][$feature][$value]) \|\|
131			$this->discreteProb[$label][$feature][$value] == 0) {
132			return self::EPSILON;
133			}
134			return $this->discreteProb[$label][$feature][$value];
135			}
136			$std = $this->std[$label][$feature] ;
137			$mean= $this->mean[$label][$feature];
138			// Calculate the probability density by use of normal/Gaussian distribution
139			// Ref: https://en.wikipedia.org/wiki/Normal_distribution
140			//
141			// In order to avoid numerical errors because of small or zero values,
142			// some libraries adopt taking log of calculations such as
143			// scikit-learn did.
144			// (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
145			$pdf = -0.5 * log(2.0 * pi() * $std * $std);
146			$pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
147			return $pdf;
148			}
149
150			/**
151			* Return samples belonging to specific label
152			* @param string $label
153			* @return array
154			*/
155			private function getSamplesByLabel($label)
156			{
157			$samples = [];
158			for ($i=0; $i<$this->sampleCount; $i++) {
159			if ($this->targets[$i] == $label) {
160			$samples[] = $this->samples[$i];
161			}
162			}
163			return $samples;
164			}
165
166			/**
167			* @param array $sample
168			* @return mixed
169			*/
170			protected function predictSample(array $sample)
171			{
172			// Use NaiveBayes assumption for each label using:
173			// P(label\|features) = P(label) * P(feature0\|label) * P(feature1\|label) .... P(featureN\|label)
174			// Then compare probability for each class to determine which label is most likely
175			<<<<<<< HEAD
176			$predictions = array();
177			=======
178			$predictions = [];
179			>>>>>>> refs/remotes/php-ai/master
180			foreach ($this->labels as $label) {
181			$p = $this->p[$label];
182			for ($i=0; $i<$this->featureCount; $i++) {
183			$Plf = $this->sampleProbability($sample, $i, $label);
184			$p += $Plf;
185			}
186			$predictions[$label] = $p;
187			}
188			arsort($predictions, SORT_NUMERIC);
189			reset($predictions);
190			return key($predictions);
191			}
192			}
193

php-ai / php-ml

Pull Request — master (#36)

NaiveBayes::calculateStatistics() B

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