NaiveBayes::predictSample() - Code Metrics - Inspection of "DecisionTree and Fuzzy C Means classifiers (#35)" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Completed

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by Arkadiusz

created 2017-01-31 19:27 UTC

NaiveBayes::predictSample() A

↳ Parent: NaiveBayes

Complexity

Conditions	3
Paths	3

Size

Total Lines	18
Code Lines	11

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	18
rs	9.4285
c	0
b	0
f	0
cc	3
eloc	11
nc	3
nop	1

<?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 = $samples;
        $this->targets = $targets;
        $this->sampleCount = count($samples);
        $this->featureCount = count($samples[0]);

        $labelCounts = array_count_values($targets);
        $this->labels = array_keys($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)
    {
        $samples = array();
        for ($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
        $predictions = array();
        foreach ($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
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 = $samples;
67			$this->targets = $targets;
68			$this->sampleCount = count($samples);
69			$this->featureCount = count($samples[0]);
70
71			$labelCounts = array_count_values($targets);
72			$this->labels = array_keys($labelCounts);
73			foreach ($this->labels as $label) {
74			$samples = $this->getSamplesByLabel($label);
75			$this->p[$label] = count($samples) / $this->sampleCount;
76			$this->calculateStatistics($label, $samples);
77			}
78			}
79
80			/**
81			* Calculates vital statistics for each label & feature. Stores these
82			* values in private array in order to avoid repeated calculation
83			* @param string $label
84			* @param array $samples
85			*/
86			private function calculateStatistics($label, $samples)
87			{
88			$this->std[$label] = array_fill(0, $this->featureCount, 0);
89			$this->mean[$label]= array_fill(0, $this->featureCount, 0);
90			$this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
91			$this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
92			for ($i=0; $i<$this->featureCount; $i++) {
93			// Get the values of nth column in the samples array
94			// Mean::arithmetic is called twice, can be optimized
95			$values = array_column($samples, $i);
96			$numValues = count($values);
97			// if the values contain non-numeric data,
98			// then it should be treated as nominal/categorical/discrete column
99			if ($values !== array_filter($values, 'is_numeric')) {
100			$this->dataType[$label][$i] = self::NOMINAL;
101			$this->discreteProb[$label][$i] = array_count_values($values);
102			$db = &$this->discreteProb[$label][$i];
103			$db = array_map(function ($el) use ($numValues) {
104			return $el / $numValues;
105			}, $db);
106			} else {
107			$this->mean[$label][$i] = Mean::arithmetic($values);
108			// Add epsilon in order to avoid zero stdev
109			$this->std[$label][$i] = 1e-10 + StandardDeviation::population($values, false);
110			}
111			}
112			}
113
114			/**
115			* Calculates the probability P(label\|sample_n)
116			*
117			* @param array $sample
118			* @param int $feature
119			* @param string $label
120			* @return float
121			*/
122			private function sampleProbability($sample, $feature, $label)
123			{
124			$value = $sample[$feature];
125			if ($this->dataType[$label][$feature] == self::NOMINAL) {
126			if (! isset($this->discreteProb[$label][$feature][$value]) \|\|
127			$this->discreteProb[$label][$feature][$value] == 0) {
128			return self::EPSILON;
129			}
130			return $this->discreteProb[$label][$feature][$value];
131			}
132			$std = $this->std[$label][$feature] ;
133			$mean= $this->mean[$label][$feature];
134			// Calculate the probability density by use of normal/Gaussian distribution
135			// Ref: https://en.wikipedia.org/wiki/Normal_distribution
136			//
137			// In order to avoid numerical errors because of small or zero values,
138			// some libraries adopt taking log of calculations such as
139			// scikit-learn did.
140			// (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
141			$pdf = -0.5 * log(2.0 * pi() * $std * $std);
142			$pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
143			return $pdf;
144			}
145
146			/**
147			* Return samples belonging to specific label
148			* @param string $label
149			* @return array
150			*/
151			private function getSamplesByLabel($label)
152			{
153			$samples = array();
154			for ($i=0; $i<$this->sampleCount; $i++) {
155			if ($this->targets[$i] == $label) {
156			$samples[] = $this->samples[$i];
157			}
158			}
159			return $samples;
160			}
161
162			/**
163			* @param array $sample
164			* @return mixed
165			*/
166			protected function predictSample(array $sample)
167			{
168			// Use NaiveBayes assumption for each label using:
169			// P(label\|features) = P(label) * P(feature0\|label) * P(feature1\|label) .... P(featureN\|label)
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170			// Then compare probability for each class to determine which label is most likely
171			$predictions = array();
172			foreach ($this->labels as $label) {
173			$p = $this->p[$label];
174			for ($i=0; $i<$this->featureCount; $i++) {
175			$Plf = $this->sampleProbability($sample, $i, $label);
176			$p += $Plf;
177			}
178			$predictions[$label] = $p;
179			}
180			arsort($predictions, SORT_NUMERIC);
181			reset($predictions);
182			return key($predictions);
183			}
184			}
185

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NaiveBayes::predictSample() A

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