Apriori::antecedents() - Code Metrics - Inspection of "Add .gitattributes" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#287)

by Marcin

created 2018-06-24 18:53 UTC

Apriori::antecedents() A

↳ Parent: Apriori

Complexity

Conditions	2
Paths	1

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	9
rs	9.9666
c	0
b	0
f	0
cc	2
nc	1
nop	1

<?php

declare(strict_types=1);

namespace Phpml\Association;

use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;

class Apriori implements Associator
{
    use Trainable, Predictable;

    public const ARRAY_KEY_ANTECEDENT = 'antecedent';

    public const ARRAY_KEY_CONFIDENCE = 'confidence';

    public const ARRAY_KEY_CONSEQUENT = 'consequent';

    public const ARRAY_KEY_SUPPORT = 'support';

    /**
     * Minimum relative probability of frequent transactions.
     *
     * @var float
     */
    private $confidence;

    /**
     * The large set contains frequent k-length item sets.
     *
     * @var mixed[][][]
     */
    private $large = [];

    /**
     * Minimum relative frequency of transactions.
     *
     * @var float
     */
    private $support;

    /**
     * The generated Apriori association rules.
     *
     * @var mixed[][]
     */
    private $rules = [];

    /**
     * Apriori constructor.
     */
    public function __construct(float $support = 0.0, float $confidence = 0.0)
    {
        $this->support = $support;
        $this->confidence = $confidence;
    }

    /**
     * Get all association rules which are generated for every k-length frequent item set.
     *
     * @return mixed[][]
     */
    public function getRules(): array
    {
        if (empty($this->large)) {
            $this->large = $this->apriori();
        }

        if (!empty($this->rules)) {
            return $this->rules;
        }

        $this->rules = [];

        $this->generateAllRules();

        return $this->rules;
    }

    /**
     * Generates frequent item sets.
     *
     * @return mixed[][][]
     */
    public function apriori(): array
    {
        $L = [];

        $items = $this->frequent($this->items());
        for ($k = 1; !empty($items); ++$k) {
            $L[$k] = $items;
            $items = $this->frequent($this->candidates($items));
        }

        return $L;
    }

    /**
     * @param mixed[] $sample
     *
     * @return mixed[][]
     */
    protected function predictSample(array $sample): array
    {
        $predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample) {
            return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample);
        }));

        return array_map(function ($rule) {
            return $rule[self::ARRAY_KEY_CONSEQUENT];
        }, $predicts);
    }

    /**
     * Generate rules for each k-length frequent item set.
     */
    private function generateAllRules(): void
    {
        for ($k = 2; !empty($this->large[$k]); ++$k) {
            foreach ($this->large[$k] as $frequent) {
                $this->generateRules($frequent);
            }
        }
    }

    /**
     * Generate confident rules for frequent item set.
     *
     * @param mixed[] $frequent
     */
    private function generateRules(array $frequent): void
    {
        foreach ($this->antecedents($frequent) as $antecedent) {
            $confidence = $this->confidence($frequent, $antecedent);
            if ($this->confidence <= $confidence) {
                $consequent = array_values(array_diff($frequent, $antecedent));
                $this->rules[] = [
                    self::ARRAY_KEY_ANTECEDENT => $antecedent,
                    self::ARRAY_KEY_CONSEQUENT => $consequent,
                    self::ARRAY_KEY_SUPPORT => $this->support($frequent),
                    self::ARRAY_KEY_CONFIDENCE => $confidence,
                ];
            }
        }
    }

    /**
     * Generates the power set for given item set $sample.
     *
     * @param mixed[] $sample
     *
     * @return mixed[][]
     */
    private function powerSet(array $sample): array
    {
        $results = [[]];
        foreach ($sample as $item) {
            foreach ($results as $combination) {
                $results[] = array_merge([$item], $combination);
            }
        }

        return $results;
    }

    /**
     * Generates all proper subsets for given set $sample without the empty set.
     *
     * @param mixed[] $sample
     *
     * @return mixed[][]
     */
    private function antecedents(array $sample): array
    {
        $cardinality = count($sample);
        $antecedents = $this->powerSet($sample);

        return array_filter($antecedents, function ($antecedent) use ($cardinality) {
            return (count($antecedent) != $cardinality) && ($antecedent != []);
        });
    }

    /**
     * Calculates frequent k = 1 item sets.
     *
     * @return mixed[][]
     */
    private function items(): array
    {
        $items = [];

        foreach ($this->samples as $sample) {
            foreach ($sample as $item) {
                if (!in_array($item, $items, true)) {
                    $items[] = $item;
                }
            }
        }

        return array_map(function ($entry) {
            return [$entry];
        }, $items);
    }

    /**
     * Returns frequent item sets only.
     *
     * @param mixed[][] $samples
     *
     * @return mixed[][]
     */
    private function frequent(array $samples): array
    {
        return array_values(array_filter($samples, function ($entry) {
            return $this->support($entry) >= $this->support;
        }));
    }

    /**
     * Calculates frequent k item sets, where count($samples) == $k - 1.
     *
     * @param mixed[][] $samples
     *
     * @return mixed[][]
     */
    private function candidates(array $samples): array
    {
        $candidates = [];

        foreach ($samples as $p) {
            foreach ($samples as $q) {
                if (count(array_merge(array_diff($p, $q), array_diff($q, $p))) != 2) {
                    continue;
                }

                $candidate = array_values(array_unique(array_merge($p, $q)));

                if ($this->contains($candidates, $candidate)) {
                    continue;
                }

                foreach ((array) $this->samples as $sample) {
                    if ($this->subset($sample, $candidate)) {
                        $candidates[] = $candidate;

                        continue 2;
                    }
                }
            }
        }

        return $candidates;
    }

    /**
     * Calculates confidence for $set. Confidence is the relative amount of sets containing $subset which also contain
     * $set.
     *
     * @param mixed[] $set
     * @param mixed[] $subset
     */
    private function confidence(array $set, array $subset): float
    {
        return $this->support($set) / $this->support($subset);
    }

    /**
     * Calculates support for item set $sample. Support is the relative amount of sets containing $sample in the data
     * pool.
     *
     * @see \Phpml\Association\Apriori::samples
     *
     * @param mixed[] $sample
     */
    private function support(array $sample): float
    {
        return $this->frequency($sample) / count($this->samples);
    }

    /**
     * Counts occurrences of $sample as subset in data pool.
     *
     * @see \Phpml\Association\Apriori::samples
     *
     * @param mixed[] $sample
     */
    private function frequency(array $sample): int
    {
        return count(array_filter($this->samples, function ($entry) use ($sample) {
            return $this->subset($entry, $sample);
        }));
    }

    /**
     * Returns true if set is an element of system.
     *
     * @see \Phpml\Association\Apriori::equals()
     *
     * @param mixed[][] $system
     * @param mixed[]   $set
     */
    private function contains(array $system, array $set): bool
    {
        return (bool) array_filter($system, function ($entry) use ($set) {
            return $this->equals($entry, $set);
        });
    }

    /**
     * Returns true if subset is a (proper) subset of set by its items string representation.
     *
     * @param mixed[] $set
     * @param mixed[] $subset
     */
    private function subset(array $set, array $subset): bool
    {
        return !array_diff($subset, array_intersect($subset, $set));
    }

    /**
     * Returns true if string representation of items does not differ.
     *
     * @param mixed[] $set1
     * @param mixed[] $set2
     */
    private function equals(array $set1, array $set2): bool
    {
        return array_diff($set1, $set2) == array_diff($set2, $set1);
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Association;
6
7			use Phpml\Helper\Predictable;
8			use Phpml\Helper\Trainable;
9
10			class Apriori implements Associator
11			{
12			use Trainable, Predictable;
13
14			public const ARRAY_KEY_ANTECEDENT = 'antecedent';
15
16			public const ARRAY_KEY_CONFIDENCE = 'confidence';
17
18			public const ARRAY_KEY_CONSEQUENT = 'consequent';
19
20			public const ARRAY_KEY_SUPPORT = 'support';
21
22			/**
23			* Minimum relative probability of frequent transactions.
24			*
25			* @var float
26			*/
27			private $confidence;
28
29			/**
30			* The large set contains frequent k-length item sets.
31			*
32			* @var mixed[][][]
33			*/
34			private $large = [];
35
36			/**
37			* Minimum relative frequency of transactions.
38			*
39			* @var float
40			*/
41			private $support;
42
43			/**
44			* The generated Apriori association rules.
45			*
46			* @var mixed[][]
47			*/
48			private $rules = [];
49
50			/**
51			* Apriori constructor.
52			*/
53			public function __construct(float $support = 0.0, float $confidence = 0.0)
54			{
55			$this->support = $support;
56			$this->confidence = $confidence;
57			}
58
59			/**
60			* Get all association rules which are generated for every k-length frequent item set.
61			*
62			* @return mixed[][]
63			*/
64			public function getRules(): array
65			{
66			if (empty($this->large)) {
67			$this->large = $this->apriori();
68			}
69
70			if (!empty($this->rules)) {
71			return $this->rules;
72			}
73
74			$this->rules = [];
75
76			$this->generateAllRules();
77
78			return $this->rules;
79			}
80
81			/**
82			* Generates frequent item sets.
83			*
84			* @return mixed[][][]
85			*/
86			public function apriori(): array
87			{
88			$L = [];
89
90			$items = $this->frequent($this->items());
91			for ($k = 1; !empty($items); ++$k) {
92			$L[$k] = $items;
93			$items = $this->frequent($this->candidates($items));
94			}
95
96			return $L;
97			}
98
99			/**
100			* @param mixed[] $sample
101			*
102			* @return mixed[][]
103			*/
104			protected function predictSample(array $sample): array
105			{
106			$predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample) {
107			return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample);
108			}));
109
110			return array_map(function ($rule) {
111			return $rule[self::ARRAY_KEY_CONSEQUENT];
112			}, $predicts);
113			}
114
115			/**
116			* Generate rules for each k-length frequent item set.
117			*/
118			private function generateAllRules(): void
119			{
120			for ($k = 2; !empty($this->large[$k]); ++$k) {
121			foreach ($this->large[$k] as $frequent) {
122			$this->generateRules($frequent);
123			}
124			}
125			}
126
127			/**
128			* Generate confident rules for frequent item set.
129			*
130			* @param mixed[] $frequent
131			*/
132			private function generateRules(array $frequent): void
133			{
134			foreach ($this->antecedents($frequent) as $antecedent) {
135			$confidence = $this->confidence($frequent, $antecedent);
136			if ($this->confidence <= $confidence) {
137			$consequent = array_values(array_diff($frequent, $antecedent));
138			$this->rules[] = [
139			self::ARRAY_KEY_ANTECEDENT => $antecedent,
140			self::ARRAY_KEY_CONSEQUENT => $consequent,
141			self::ARRAY_KEY_SUPPORT => $this->support($frequent),
142			self::ARRAY_KEY_CONFIDENCE => $confidence,
143			];
144			}
145			}
146			}
147
148			/**
149			* Generates the power set for given item set $sample.
150			*
151			* @param mixed[] $sample
152			*
153			* @return mixed[][]
154			*/
155			private function powerSet(array $sample): array
156			{
157			$results = [[]];
158			foreach ($sample as $item) {
159			foreach ($results as $combination) {
160			$results[] = array_merge([$item], $combination);
161			}
162			}
163
164			return $results;
165			}
166
167			/**
168			* Generates all proper subsets for given set $sample without the empty set.
169			*
170			* @param mixed[] $sample
171			*
172			* @return mixed[][]
173			*/
174			private function antecedents(array $sample): array
175			{
176			$cardinality = count($sample);
177			$antecedents = $this->powerSet($sample);
178
179			return array_filter($antecedents, function ($antecedent) use ($cardinality) {
180			return (count($antecedent) != $cardinality) && ($antecedent != []);
181			});
182			}
183
184			/**
185			* Calculates frequent k = 1 item sets.
186			*
187			* @return mixed[][]
188			*/
189			private function items(): array
190			{
191			$items = [];
192
193			foreach ($this->samples as $sample) {
194			foreach ($sample as $item) {
195			if (!in_array($item, $items, true)) {
196			$items[] = $item;
197			}
198			}
199			}
200
201			return array_map(function ($entry) {
202			return [$entry];
203			}, $items);
204			}
205
206			/**
207			* Returns frequent item sets only.
208			*
209			* @param mixed[][] $samples
210			*
211			* @return mixed[][]
212			*/
213			private function frequent(array $samples): array
214			{
215			return array_values(array_filter($samples, function ($entry) {
216			return $this->support($entry) >= $this->support;
217			}));
218			}
219
220			/**
221			* Calculates frequent k item sets, where count($samples) == $k - 1.
222			*
223			* @param mixed[][] $samples
224			*
225			* @return mixed[][]
226			*/
227			private function candidates(array $samples): array
228			{
229			$candidates = [];
230
231			foreach ($samples as $p) {
232			foreach ($samples as $q) {
233			if (count(array_merge(array_diff($p, $q), array_diff($q, $p))) != 2) {
234			continue;
235			}
236
237			$candidate = array_values(array_unique(array_merge($p, $q)));
238
239			if ($this->contains($candidates, $candidate)) {
240			continue;
241			}
242
243			foreach ((array) $this->samples as $sample) {
244			if ($this->subset($sample, $candidate)) {
245			$candidates[] = $candidate;
246
247			continue 2;
248			}
249			}
250			}
251			}
252
253			return $candidates;
254			}
255
256			/**
257			* Calculates confidence for $set. Confidence is the relative amount of sets containing $subset which also contain
258			* $set.
259			*
260			* @param mixed[] $set
261			* @param mixed[] $subset
262			*/
263			private function confidence(array $set, array $subset): float
264			{
265			return $this->support($set) / $this->support($subset);
266			}
267
268			/**
269			* Calculates support for item set $sample. Support is the relative amount of sets containing $sample in the data
270			* pool.
271			*
272			* @see \Phpml\Association\Apriori::samples
273			*
274			* @param mixed[] $sample
275			*/
276			private function support(array $sample): float
277			{
278			return $this->frequency($sample) / count($this->samples);
279			}
280
281			/**
282			* Counts occurrences of $sample as subset in data pool.
283			*
284			* @see \Phpml\Association\Apriori::samples
285			*
286			* @param mixed[] $sample
287			*/
288			private function frequency(array $sample): int
289			{
290			return count(array_filter($this->samples, function ($entry) use ($sample) {
291			return $this->subset($entry, $sample);
292			}));
293			}
294
295			/**
296			* Returns true if set is an element of system.
297			*
298			* @see \Phpml\Association\Apriori::equals()
299			*
300			* @param mixed[][] $system
301			* @param mixed[] $set
302			*/
303			private function contains(array $system, array $set): bool
304			{
305			return (bool) array_filter($system, function ($entry) use ($set) {
306			return $this->equals($entry, $set);
307			});
308			}
309
310			/**
311			* Returns true if subset is a (proper) subset of set by its items string representation.
312			*
313			* @param mixed[] $set
314			* @param mixed[] $subset
315			*/
316			private function subset(array $set, array $subset): bool
317			{
318			return !array_diff($subset, array_intersect($subset, $set));
319			}
320
321			/**
322			* Returns true if string representation of items does not differ.
323			*
324			* @param mixed[] $set1
325			* @param mixed[] $set2
326			*/
327			private function equals(array $set1, array $set2): bool
328			{
329			return array_diff($set1, $set2) == array_diff($set2, $set1);
330			}
331			}
332

php-ai / php-ml

Pull Request — master (#287)

Apriori::antecedents() A

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