Pipeline is a meta-estimator capable of transforming an input dataset by applying a series of Transformer middleware. Under the hood, Pipeline will automatically fit the training set and transform any Dataset object supplied as an argument to one of the base estimator's methods before reaching the method context. With elastic mode enabled, Pipeline will update the fitting of Elastic transformers during partial training.
Note: Since transformations are applied to dataset objects in-place (without making a copy of 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 that consumes it.
Data Type Compatibility: Depends on base learner and transformers
|1||transformers||array||A list of transformers to be applied in order.|
|2||estimator||Estimator||An instance of a base estimator to receive the transformed data.|
|3||elastic||true||bool||Should we update the elastic transformers during partial training?|
use Rubix\ML\Pipeline; use Rubix\ML\Transformers\MissingDataImputer; use Rubix\ML\Transformers\OneHotEncoder; use Rubix\ML\Transformers\PrincipalComponentAnalysis; use Rubix\ML\Classifiers\SoftmaxClassifier; $estimator = new Pipeline([ new MissingDataImputer(), new OneHotEncoder(), new PrincipalComponentAnalysis(20), ], new SoftmaxClassifier(128), true);
This meta-estimator does not have any additional methods.