66 lines
1.7 KiB
Markdown
66 lines
1.7 KiB
Markdown
# Pipeline
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In machine learning, it is common to run a sequence of algorithms to process and learn from dataset. For example:
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* Split each document’s text into tokens.
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* Convert each document’s words into a numerical feature vector ([Token Count Vectorizer](machine-learning/feature-extraction/token-count-vectorizer/)).
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* Learn a prediction model using the feature vectors and labels.
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PHP-ML represents such a workflow as a Pipeline, which consists of a sequence of transformers and an estimator.
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### Constructor Parameters
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* $transformers (array|Transformer[]) - sequence of objects that implements the Transformer interface
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* $estimator (Estimator) - estimator that can train and predict
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```
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use Phpml\Classification\SVC;
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use Phpml\FeatureExtraction\TfIdfTransformer;
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use Phpml\Pipeline;
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$transformers = [
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new TfIdfTransformer(),
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];
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$estimator = new SVC();
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$pipeline = new Pipeline($transformers, $estimator);
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```
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### Example
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First, our pipeline replaces the missing value, then normalizes samples and finally trains the SVC estimator.
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Thus prepared pipeline repeats each transformation step for predicted sample.
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```
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use Phpml\Classification\SVC;
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use Phpml\Pipeline;
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use Phpml\Preprocessing\Imputer;
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use Phpml\Preprocessing\Normalizer;
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use Phpml\Preprocessing\Imputer\Strategy\MostFrequentStrategy;
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$transformers = [
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new Imputer(null, new MostFrequentStrategy()),
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new Normalizer(),
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];
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$estimator = new SVC();
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$samples = [
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[1, -1, 2],
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[2, 0, null],
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[null, 1, -1],
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];
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$targets = [
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4,
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1,
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4,
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];
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$pipeline = new Pipeline($transformers, $estimator);
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$pipeline->train($samples, $targets);
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$predicted = $pipeline->predict([[0, 0, 0]]);
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// $predicted == 4
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```
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