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

"""Stacked Ensemble Base."""

from evalml.model_family import ModelFamily
from evalml.pipelines.components import Estimator
from evalml.utils import classproperty

_nonstackable_model_families = [
    ModelFamily.BASELINE,
    ModelFamily.VOWPAL_WABBIT,
    ModelFamily.NONE,
]


[docs]class StackedEnsembleBase(Estimator): """Stacked Ensemble Base Class. Arguments: final_estimator (Estimator or subclass): The estimator used to combine the base estimators. n_jobs (int or None): Integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs greater than -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. """ model_family = ModelFamily.ENSEMBLE """ModelFamily.ENSEMBLE""" _default_final_estimator = None _can_be_used_for_fast_partial_dependence = False def __init__( self, final_estimator=None, n_jobs=-1, random_seed=0, **kwargs, ): final_estimator = final_estimator or self._default_final_estimator() parameters = { "final_estimator": final_estimator, "n_jobs": n_jobs, } parameters.update(kwargs) super().__init__( parameters=parameters, component_obj=final_estimator, random_seed=random_seed, ) @property def feature_importance(self): """Not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor.""" raise NotImplementedError( "feature_importance is not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor", ) @classproperty def default_parameters(cls): """Returns the default parameters for stacked ensemble classes. Returns: dict: default parameters for this component. """ return { "final_estimator": cls._default_final_estimator, "n_jobs": -1, }