ClassificationReport::getLabelIndexedArray() - Code Metrics - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

ClassificationReport::getLabelIndexedArray() A
last analyzed 2020-05-15 05:48 UTC

↳ Parent: ClassificationReport

Complexity

Conditions	1
Paths	1

Size

Total Lines	6
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	3
dl	0
loc	6
rs	10
c	0
b	0
f	0
cc	1
nc	1
nop	2

<?php

declare(strict_types=1);

namespace Phpml\Metric;

use Phpml\Exception\InvalidArgumentException;

class ClassificationReport
{
    public const MICRO_AVERAGE = 1;

    public const MACRO_AVERAGE = 2;

    public const WEIGHTED_AVERAGE = 3;

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

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

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

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

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

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

    /**
     * @var array
     */
    private $f1score = [];

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

    public function __construct(array $actualLabels, array $predictedLabels, int $average = self::MACRO_AVERAGE)
    {
        $averagingMethods = range(self::MICRO_AVERAGE, self::WEIGHTED_AVERAGE);
        if (!in_array($average, $averagingMethods, true)) {
            throw new InvalidArgumentException('Averaging method must be MICRO_AVERAGE, MACRO_AVERAGE or WEIGHTED_AVERAGE');
        }

        $this->aggregateClassificationResults($actualLabels, $predictedLabels);
        $this->computeMetrics();
        $this->computeAverage($average);
    }

    public function getPrecision(): array
    {
        return $this->precision;
    }

    public function getRecall(): array
    {
        return $this->recall;
    }

    public function getF1score(): array
    {
        return $this->f1score;
    }

    public function getSupport(): array
    {
        return $this->support;
    }

    public function getAverage(): array
    {
        return $this->average;
    }

    private function aggregateClassificationResults(array $actualLabels, array $predictedLabels): void
    {
        $truePositive = $falsePositive = $falseNegative = $support = self::getLabelIndexedArray($actualLabels, $predictedLabels);

        foreach ($actualLabels as $index => $actual) {
            $predicted = $predictedLabels[$index];
            ++$support[$actual];

            if ($actual === $predicted) {
                ++$truePositive[$actual];
            } else {
                ++$falsePositive[$predicted];
                ++$falseNegative[$actual];
            }
        }

        $this->truePositive = $truePositive;
        $this->falsePositive = $falsePositive;
        $this->falseNegative = $falseNegative;
        $this->support = $support;
    }

    private function computeMetrics(): void
    {
        foreach ($this->truePositive as $label => $tp) {
            $this->precision[$label] = $this->computePrecision($tp, $this->falsePositive[$label]);
            $this->recall[$label] = $this->computeRecall($tp, $this->falseNegative[$label]);
            $this->f1score[$label] = $this->computeF1Score((float) $this->precision[$label], (float) $this->recall[$label]);
        }
    }

    private function computeAverage(int $average): void
    {
        switch ($average) {
            case self::MICRO_AVERAGE:
                $this->computeMicroAverage();

                return;
            case self::MACRO_AVERAGE:
                $this->computeMacroAverage();

                return;
            case self::WEIGHTED_AVERAGE:
                $this->computeWeightedAverage();

                return;
        }
    }

    private function computeMicroAverage(): void
    {
        $truePositive = (int) array_sum($this->truePositive);
        $falsePositive = (int) array_sum($this->falsePositive);
        $falseNegative = (int) array_sum($this->falseNegative);

        $precision = $this->computePrecision($truePositive, $falsePositive);
        $recall = $this->computeRecall($truePositive, $falseNegative);
        $f1score = $this->computeF1Score($precision, $recall);

        $this->average = compact('precision', 'recall', 'f1score');
    }

    private function computeMacroAverage(): void
    {
        foreach (['precision', 'recall', 'f1score'] as $metric) {
            $values = $this->{$metric};
            if (count($values) == 0) {
                $this->average[$metric] = 0.0;

                continue;
            }

            $this->average[$metric] = array_sum($values) / count($values);
        }
    }

    private function computeWeightedAverage(): void
    {
        foreach (['precision', 'recall', 'f1score'] as $metric) {
            $values = $this->{$metric};
            if (count($values) == 0) {
                $this->average[$metric] = 0.0;

                continue;
            }

            $sum = 0;
            foreach ($values as $i => $value) {
                $sum += $value * $this->support[$i];
            }

            $this->average[$metric] = $sum / array_sum($this->support);
        }
    }

    private function computePrecision(int $truePositive, int $falsePositive): float
    {
        $divider = $truePositive + $falsePositive;
        if ($divider == 0) {
            return 0.0;
        }

        return $truePositive / $divider;
    }

    private function computeRecall(int $truePositive, int $falseNegative): float
    {
        $divider = $truePositive + $falseNegative;
        if ($divider == 0) {
            return 0.0;
        }

        return $truePositive / $divider;
    }

    private function computeF1Score(float $precision, float $recall): float
    {
        $divider = $precision + $recall;
        if ($divider == 0) {
            return 0.0;
        }

        return 2.0 * (($precision * $recall) / $divider);
    }

    private static function getLabelIndexedArray(array $actualLabels, array $predictedLabels): array
    {
        $labels = array_values(array_unique(array_merge($actualLabels, $predictedLabels)));
        sort($labels);

        return (array) array_combine($labels, array_fill(0, count($labels), 0));
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Metric;
6
7			use Phpml\Exception\InvalidArgumentException;
8
9			class ClassificationReport
10			{
11			public const MICRO_AVERAGE = 1;
12
13			public const MACRO_AVERAGE = 2;
14
15			public const WEIGHTED_AVERAGE = 3;
16
17			/**
18			* @var array
19			*/
20			private $truePositive = [];
21
22			/**
23			* @var array
24			*/
25			private $falsePositive = [];
26
27			/**
28			* @var array
29			*/
30			private $falseNegative = [];
31
32			/**
33			* @var array
34			*/
35			private $support = [];
36
37			/**
38			* @var array
39			*/
40			private $precision = [];
41
42			/**
43			* @var array
44			*/
45			private $recall = [];
46
47			/**
48			* @var array
49			*/
50			private $f1score = [];
51
52			/**
53			* @var array
54			*/
55			private $average = [];
56
57			public function __construct(array $actualLabels, array $predictedLabels, int $average = self::MACRO_AVERAGE)
58			{
59			$averagingMethods = range(self::MICRO_AVERAGE, self::WEIGHTED_AVERAGE);
60			if (!in_array($average, $averagingMethods, true)) {
61			throw new InvalidArgumentException('Averaging method must be MICRO_AVERAGE, MACRO_AVERAGE or WEIGHTED_AVERAGE');
62			}
63
64			$this->aggregateClassificationResults($actualLabels, $predictedLabels);
65			$this->computeMetrics();
66			$this->computeAverage($average);
67			}
68
69			public function getPrecision(): array
70			{
71			return $this->precision;
72			}
73
74			public function getRecall(): array
75			{
76			return $this->recall;
77			}
78
79			public function getF1score(): array
80			{
81			return $this->f1score;
82			}
83
84			public function getSupport(): array
85			{
86			return $this->support;
87			}
88
89			public function getAverage(): array
90			{
91			return $this->average;
92			}
93
94			private function aggregateClassificationResults(array $actualLabels, array $predictedLabels): void
95			{
96			$truePositive = $falsePositive = $falseNegative = $support = self::getLabelIndexedArray($actualLabels, $predictedLabels);
97
98			foreach ($actualLabels as $index => $actual) {
99			$predicted = $predictedLabels[$index];
100			++$support[$actual];
101
102			if ($actual === $predicted) {
103			++$truePositive[$actual];
104			} else {
105			++$falsePositive[$predicted];
106			++$falseNegative[$actual];
107			}
108			}
109
110			$this->truePositive = $truePositive;
111			$this->falsePositive = $falsePositive;
112			$this->falseNegative = $falseNegative;
113			$this->support = $support;
114			}
115
116			private function computeMetrics(): void
117			{
118			foreach ($this->truePositive as $label => $tp) {
119			$this->precision[$label] = $this->computePrecision($tp, $this->falsePositive[$label]);
120			$this->recall[$label] = $this->computeRecall($tp, $this->falseNegative[$label]);
121			$this->f1score[$label] = $this->computeF1Score((float) $this->precision[$label], (float) $this->recall[$label]);
122			}
123			}
124
125			private function computeAverage(int $average): void
126			{
127			switch ($average) {
128			case self::MICRO_AVERAGE:
129			$this->computeMicroAverage();
130
131			return;
132			case self::MACRO_AVERAGE:
133			$this->computeMacroAverage();
134
135			return;
136			case self::WEIGHTED_AVERAGE:
137			$this->computeWeightedAverage();
138
139			return;
140			}
141			}
142
143			private function computeMicroAverage(): void
144			{
145			$truePositive = (int) array_sum($this->truePositive);
146			$falsePositive = (int) array_sum($this->falsePositive);
147			$falseNegative = (int) array_sum($this->falseNegative);
148
149			$precision = $this->computePrecision($truePositive, $falsePositive);
150			$recall = $this->computeRecall($truePositive, $falseNegative);
151			$f1score = $this->computeF1Score($precision, $recall);
152
153			$this->average = compact('precision', 'recall', 'f1score');
154			}
155
156			private function computeMacroAverage(): void
157			{
158			foreach (['precision', 'recall', 'f1score'] as $metric) {
159			$values = $this->{$metric};
160			if (count($values) == 0) {
161			$this->average[$metric] = 0.0;
162
163			continue;
164			}
165
166			$this->average[$metric] = array_sum($values) / count($values);
167			}
168			}
169
170			private function computeWeightedAverage(): void
171			{
172			foreach (['precision', 'recall', 'f1score'] as $metric) {
173			$values = $this->{$metric};
174			if (count($values) == 0) {
175			$this->average[$metric] = 0.0;
176
177			continue;
178			}
179
180			$sum = 0;
181			foreach ($values as $i => $value) {
182			$sum += $value * $this->support[$i];
183			}
184
185			$this->average[$metric] = $sum / array_sum($this->support);
186			}
187			}
188
189			private function computePrecision(int $truePositive, int $falsePositive): float
190			{
191			$divider = $truePositive + $falsePositive;
192			if ($divider == 0) {
193			return 0.0;
194			}
195
196			return $truePositive / $divider;
197			}
198
199			private function computeRecall(int $truePositive, int $falseNegative): float
200			{
201			$divider = $truePositive + $falseNegative;
202			if ($divider == 0) {
203			return 0.0;
204			}
205
206			return $truePositive / $divider;
207			}
208
209			private function computeF1Score(float $precision, float $recall): float
210			{
211			$divider = $precision + $recall;
212			if ($divider == 0) {
213			return 0.0;
214			}
215
216			return 2.0 * (($precision * $recall) / $divider);
217			}
218
219			private static function getLabelIndexedArray(array $actualLabels, array $predictedLabels): array
220			{
221			$labels = array_values(array_unique(array_merge($actualLabels, $predictedLabels)));
222			sort($labels);
223
224			return (array) array_combine($labels, array_fill(0, count($labels), 0));
225			}
226			}
227

php-ai / php-ml

ClassificationReport::getLabelIndexedArray() A last analyzed 2020-05-15 05:48 UTC

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ClassificationReport::getLabelIndexedArray() A
last analyzed 2020-05-15 05:48 UTC