SklearnRegressorPipeline

Pipeline of transformers and a regressor.

Quickstart

from sktime.regression.compose import SklearnRegressorPipeline

estimator = SklearnRegressorPipeline(regressor, transformers)

Parameters(2)

regressorsklearn regressor, i.e., inheriting from sklearn RegressorMixin

this is a “blueprint” regressor, state does not change when fit is called

transformerslist of sktime transformers, or

list of tuples (str, transformer) of sktime transformers these are “blueprint” transformers, states do not change when fit is called

Examples

>>> from sklearn.neighbors import KNeighborsRegressor
>>> from sktime.datasets import load_unit_test
>>> from sktime.regression.compose import SklearnRegressorPipeline
>>> from sktime.transformations.exponent import ExponentTransformer
>>> from sktime.transformations.summarize import SummaryTransformer
>>> X_train, y_train = load_unit_test (split = "train")
>>> X_test, y_test = load_unit_test (split = "test")
>>> t1 = ExponentTransformer ()
>>> t2 = SummaryTransformer ()
>>> pipeline = SklearnRegressorPipeline (KNeighborsRegressor (), [t1, t2 ])
>>> pipeline = pipeline. fit (X_train, y_train)
>>> y_pred = pipeline. predict (X_test) Alternative construction via dunder method:
>>> pipeline = t1 * t2 * KNeighborsRegressor ()