make_pipeline#
- make_pipeline(*steps)[source]#
Create a pipeline from estimators of any type.
- Parameters:
- stepstuple of aeon estimators
in same order as used for pipeline construction
- Returns:
- pipeaeon pipeline containing steps, in order
always a descendant of BaseObject, precise object determined by equivalent to result of step[0] * step[1] * … * step[-1]
Examples
>>> from aeon.datasets import load_airline >>> y = load_airline()
Example 1: forecaster pipeline >>> from aeon.datasets import load_airline >>> from aeon.forecasting.trend import PolynomialTrendForecaster >>> from aeon.pipeline import make_pipeline >>> from aeon.transformations.exponent import ExponentTransformer >>> y = load_airline() >>> pipe = make_pipeline(ExponentTransformer(), PolynomialTrendForecaster()) >>> type(pipe).__name__ ‘TransformedTargetForecaster’
Example 2: classifier pipeline >>> from aeon.classification.feature_based import Catch22Classifier >>> from aeon.pipeline import make_pipeline >>> from aeon.transformations.exponent import ExponentTransformer >>> pipe = make_pipeline(ExponentTransformer(), Catch22Classifier()) >>> type(pipe).__name__ ‘ClassifierPipeline’
Example 3: transformer pipeline >>> from aeon.pipeline import make_pipeline >>> from aeon.transformations.exponent import ExponentTransformer >>> pipe = make_pipeline(ExponentTransformer(), ExponentTransformer()) >>> type(pipe).__name__ ‘TransformerPipeline’