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Pipeline#

Pipeline is a meta estimator responsible for transforming the input dataset by applying a series of transformer middleware. Under the hood, Pipeline will automatically fit the training set upon training and transform any Dataset object supplied as an argument to one of the base estimator's methods, including train() and predict(). With elastic mode enabled, Pipeline will update the fitting of elastic transformers during online (partial) training.

Note: Since transformations are applied to dataset objects in-place (without copying the data), using a dataset in a program after it has been run through Pipeline may have unexpected results. If you need to keep a clean dataset in memory you can clone the dataset object before calling the method on Pipeline.

Interfaces: Estimator, Learner, Online, Probabilistic, Persistable, Verbose

Data Type Compatibility: Depends on base learner and transformers

Parameters#

# Param Default Type Description
1 transformers array The transformer middleware to be applied to the input data in order.
2 estimator object An instance of the base estimator to receive transformed data.
3 elastic true bool Should we update the elastic transformers during partial training?

Additional Methods#

This meta estimator does not have any additional methods.

Example#

use Rubix\ML\Pipeline;
use Rubix\ML\Transformers\NumericStringConverter;
use Rubix\ML\Transformers\MissingDataImputer;
use Rubix\ML\Transformers\OneHotEncoder;
use Rubix\ML\Transformers\PrincipalComponentAnalysis;
use Rubix\ML\Classifiers\SoftmaxClassifier;

$estimator = new Pipeline([
    new NumericStringConverter(),
    new MissingDataImputer('?'),
    new OneHotEncoder(), 
    new PrincipalComponentAnalysis(20),
], new SoftmaxClassifier());