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php-ml
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* Remove user-specific gitignore
* Add return type hints
* Avoid global namespace in docs
* Rename rules -> getRules
* Split up rule generation

Todo:
* Move set theory out to math
* Extract rule generation
This commit is contained in:
Patrick Florek 2016-09-02 00:18:50 +02:00
parent c8bd8db601
commit 90038befa9
4 changed files with 73 additions and 50 deletions
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1
.gitignore vendored
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@ -1,4 +1,3 @@
/.idea/
/vendor/
humbuglog.*
/bin/phpunit

12
docs/machine-learning/association/apriori.md
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@ -8,7 +8,9 @@ Association rule learning based on [Apriori algorithm](https://en.wikipedia.org/
* $confidence - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Confidence), minimum relative amount of item set in frequent item sets
```
$associator = new \Phpml\Association\Apriori($support = 0.5, $confidence = 0.5);
use Phpml\Association\Apriori;
$associator = new Apriori($support = 0.5, $confidence = 0.5);
```
### Train
@ -19,7 +21,9 @@ To train a associator simply provide train samples and labels (as `array`). Exam
$samples = [['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta'], ['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta']];
$labels = [];
$associator = new \Phpml\Association\Apriori(0.5, 0.5);
use Phpml\Association\Apriori;
$associator = new Apriori($support = 0.5, $confidence = 0.5);
$associator->train($samples, $labels);
```
@ -37,10 +41,10 @@ $associator->predict([['alpha','epsilon'],['beta','theta']]);
### Associating
Generating association rules simply use `rules` method.
Get generated association rules simply use `rules` method.
```
$associator->rules();
$associator->getRules();
// return [['antecedent' => ['alpha', 'theta'], 'consequent' => ['beta], 'support' => 1.0, 'confidence' => 1.0], ... ]
```

106
src/Phpml/Association/Apriori.php
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@ -1,6 +1,6 @@
<?php
declare(strict_types = 1);
declare(strict_types=1);
namespace Phpml\Association;
@ -53,18 +53,18 @@ class Apriori implements Associator
* @param float $support
* @param float $confidence
*/
public function __construct($support = 0.0, $confidence = 0.0)
public function __construct(float $support = 0.0, float $confidence = 0.0)
{
$this->support = $support;
$this->support = $support;
$this->confidence = $confidence;
}
/**
* Generates apriori association rules.
* Get all association rules which are generated for every k-length frequent item set.
*
* @return mixed[][]
*/
public function rules()
public function getRules() : array
{
if (!$this->large) {
$this->large = $this->apriori();
@ -76,33 +76,19 @@ class Apriori implements Associator
$this->rules = [];
for ($k = 2; !empty($this->large[$k]); ++$k) {
foreach ($this->large[$k] as $frequent) {
foreach ($this->antecedents($frequent) as $antecedent) {
if ($this->confidence <= ($confidence = $this->confidence($frequent, $antecedent))) {
$consequent = array_values(array_diff($frequent, $antecedent));
$this->rules[] = [
self::ARRAY_KEY_ANTECEDENT => $antecedent,
self::ARRAY_KEY_CONSEQUENT => $consequent,
self::ARRAY_KEY_SUPPORT => $this->support($consequent),
self::ARRAY_KEY_CONFIDENCE => $confidence,
];
}
}
}
}
$this->generateAllRules();
return $this->rules;
}
/**
* Generates frequent item sets
* Generates frequent item sets.
*
* @return mixed[][][]
*/
public function apriori()
public function apriori() : array
{
$L = [];
$L = [];
$L[1] = $this->items();
$L[1] = $this->frequent($L[1]);
@ -119,13 +105,47 @@ class Apriori implements Associator
*
* @return mixed[][]
*/
protected function predictSample(array $sample)
protected function predictSample(array $sample) : array
{
$predicts = array_values(array_filter($this->rules(), function($rule) use ($sample) {
$predicts = array_values(array_filter($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]; }, $predicts);
return array_map(function ($rule) {
return $rule[self::ARRAY_KEY_CONSEQUENT];
}, $predicts);
}
/**
* Generate rules for each k-length frequent item set.
*/
private function generateAllRules()
{
for ($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 $frequent)
{
foreach ($this->antecedents($frequent) as $antecedent) {
if ($this->confidence <= ($confidence = $this->confidence($frequent, $antecedent))) {
$consequent = array_values(array_diff($frequent, $antecedent));
$this->rules[] = [
self::ARRAY_KEY_ANTECEDENT => $antecedent,
self::ARRAY_KEY_CONSEQUENT => $consequent,
self::ARRAY_KEY_SUPPORT => $this->support($consequent),
self::ARRAY_KEY_CONFIDENCE => $confidence,
];
}
}
}
/**
@ -135,7 +155,7 @@ class Apriori implements Associator
*
* @return mixed[][]
*/
private function powerSet(array $sample)
private function powerSet(array $sample) : array
{
$results = [[]];
foreach ($sample as $item) {
@ -154,12 +174,12 @@ class Apriori implements Associator
*
* @return mixed[][]
*/
private function antecedents(array $sample)
private function antecedents(array $sample) : array
{
$cardinality = count($sample);
$antecedents = $this->powerSet($sample);
return array_filter($antecedents, function($antecedent) use ($cardinality) {
return array_filter($antecedents, function ($antecedent) use ($cardinality) {
return (count($antecedent) != $cardinality) && ($antecedent != []);
});
}
@ -169,7 +189,7 @@ class Apriori implements Associator
*
* @return mixed[][]
*/
private function items()
private function items() : array
{
$items = [];
@ -181,7 +201,7 @@ class Apriori implements Associator
}
}
return array_map(function($entry) {
return array_map(function ($entry) {
return [$entry];
}, $items);
}
@ -193,9 +213,9 @@ class Apriori implements Associator
*
* @return mixed[][]
*/
private function frequent(array $samples)
private function frequent(array $samples) : array
{
return array_filter($samples, function($entry) {
return array_filter($samples, function ($entry) {
return $this->support($entry) >= $this->support;
});
}
@ -207,7 +227,7 @@ class Apriori implements Associator
*
* @return mixed[][]
*/
private function candidates(array $samples)
private function candidates(array $samples) : array
{
$candidates = [];
@ -223,7 +243,7 @@ class Apriori implements Associator
continue;
}
foreach ((array)$this->samples as $sample) {
foreach ((array) $this->samples as $sample) {
if ($this->subset($sample, $candidate)) {
$candidates[] = $candidate;
continue 2;
@ -244,7 +264,7 @@ class Apriori implements Associator
*
* @return float
*/
private function confidence(array $set, array $subset)
private function confidence(array $set, array $subset) : float
{
return $this->support($set) / $this->support($subset);
}
@ -259,7 +279,7 @@ class Apriori implements Associator
*
* @return float
*/
private function support(array $sample)
private function support(array $sample) : float
{
return $this->frequency($sample) / count($this->samples);
}
@ -273,9 +293,9 @@ class Apriori implements Associator
*
* @return int
*/
private function frequency(array $sample)
private function frequency(array $sample) : int
{
return count(array_filter($this->samples, function($entry) use ($sample) {
return count(array_filter($this->samples, function ($entry) use ($sample) {
return $this->subset($entry, $sample);
}));
}
@ -290,9 +310,9 @@ class Apriori implements Associator
*
* @return bool
*/
private function contains(array $system, array $set)
private function contains(array $system, array $set) : bool
{
return (bool)array_filter($system, function($entry) use ($set) {
return (bool) array_filter($system, function ($entry) use ($set) {
return $this->equals($entry, $set);
});
}
@ -305,7 +325,7 @@ class Apriori implements Associator
*
* @return bool
*/
private function subset(array $set, array $subset)
private function subset(array $set, array $subset) : bool
{
return !array_diff($subset, array_intersect($subset, $set));
}
@ -318,7 +338,7 @@ class Apriori implements Associator
*
* @return bool
*/
private function equals(array $set1, array $set2)
private function equals(array $set1, array $set2) : bool
{
return array_diff($set1, $set2) == array_diff($set2, $set1);
}

4
tests/Phpml/Association/AprioriTest.php
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@ -71,12 +71,12 @@ class AprioriTest extends \PHPUnit_Framework_TestCase
$this->assertTrue($this->invoke($apriori, 'contains', [$L[2], [3, 4]]));
}
public function testRules()
public function testGetRules()
{
$apriori = new Apriori(0.4, 0.8);
$apriori->train($this->sampleChars, []);
$this->assertCount(19, $apriori->rules());
$this->assertCount(19, $apriori->getRules());
}
public function testAntecedents()