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* Multiple training data sets allowed * Tests with multiple training data sets * Updating docs according to #38 Documenting all models which predictions will be based on all training data provided. Some models already supported multiple training data sets.
61 lines
1.8 KiB
Markdown
61 lines
1.8 KiB
Markdown
# Apriori Associator
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Association rule learning based on [Apriori algorithm](https://en.wikipedia.org/wiki/Apriori_algorithm) for frequent item set mining.
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### Constructor Parameters
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* $support - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Support), minimum relative amount of frequent item set in train sample
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* $confidence - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Confidence), minimum relative amount of item set in frequent item sets
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```
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use Phpml\Association\Apriori;
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$associator = new Apriori($support = 0.5, $confidence = 0.5);
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```
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### Train
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To train a associator simply provide train samples and labels (as `array`). Example:
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```
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$samples = [['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta'], ['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta']];
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$labels = [];
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use Phpml\Association\Apriori;
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$associator = new Apriori($support = 0.5, $confidence = 0.5);
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$associator->train($samples, $labels);
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```
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You can train the associator using multiple data sets, predictions will be based on all the training data.
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### Predict
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To predict sample label use `predict` method. You can provide one sample or array of samples:
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```
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$associator->predict(['alpha','theta']);
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// return [[['beta']]]
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$associator->predict([['alpha','epsilon'],['beta','theta']]);
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// return [[['beta']], [['alpha']]]
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```
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### Associating
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Get generated association rules simply use `rules` method.
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```
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$associator->getRules();
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// return [['antecedent' => ['alpha', 'theta'], 'consequent' => ['beta], 'support' => 1.0, 'confidence' => 1.0], ... ]
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```
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### Frequent item sets
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Generating k-length frequent item sets simply use `apriori` method.
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```
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$associator->apriori();
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// return [ 1 => [['alpha'], ['beta'], ['theta'], ['epsilon']], 2 => [...], ...]
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```
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