Source code for evalml.pipelines.components.ensemble.sklearn_stacked_ensemble_regressor

"""Scikit-learn Stacked Ensemble Regressor."""
from sklearn.ensemble import StackingRegressor
from sklearn.model_selection import KFold

from evalml.model_family import ModelFamily
from evalml.pipelines.components import LinearRegressor
from evalml.pipelines.components.ensemble import SklearnStackedEnsembleBase
from evalml.problem_types import ProblemTypes


[docs]class SklearnStackedEnsembleRegressor(SklearnStackedEnsembleBase): """Scikit-learn Stacked Ensemble Regressor. Args: input_pipelines (list(PipelineBase or subclass obj)): List of pipeline instances to use as the base estimators. This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised. final_estimator (Estimator or subclass): The regressor used to combine the base estimators. If None, uses LinearRegressor. cv (int, cross-validation generator or an iterable): Determines the cross-validation splitting strategy used to train final_estimator. For int/None inputs, KFold is used. Defaults to None. Possible inputs for cv are: - None: 3-fold cross validation - int: the number of folds in a (Stratified) KFold - An scikit-learn cross-validation generator object - An iterable yielding (train, test) splits n_jobs (int or None): Non-negative integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Defaults to -1. - Note: there could be some multi-process errors thrown for values of `n_jobs != 1`. If this is the case, please use `n_jobs = 1`. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Sklearn Stacked Ensemble Regressor" model_family = ModelFamily.ENSEMBLE """ModelFamily.ENSEMBLE""" supported_problem_types = [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ] """[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ]""" hyperparameter_ranges = {} """{}""" _stacking_estimator_class = StackingRegressor _default_final_estimator = LinearRegressor _default_cv = KFold