Source code for evalml.pipelines.components.estimators.regressors.elasticnet_regressor

from sklearn.linear_model import ElasticNet as SKElasticNet
from skopt.space import Real

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes
from evalml.utils import deprecate_arg


[docs]class ElasticNetRegressor(Estimator): """Elastic Net Regressor.""" name = "Elastic Net Regressor" hyperparameter_ranges = { "alpha": Real(0, 1), "l1_ratio": Real(0, 1), } model_family = ModelFamily.LINEAR_MODEL supported_problem_types = [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION]
[docs] def __init__(self, alpha=0.5, l1_ratio=0.5, max_iter=1000, normalize=False, random_state=None, random_seed=0, **kwargs): parameters = {'alpha': alpha, 'l1_ratio': l1_ratio, 'max_iter': max_iter, 'normalize': normalize} parameters.update(kwargs) random_seed = deprecate_arg("random_state", "random_seed", random_state, random_seed) en_regressor = SKElasticNet(random_state=random_seed, **parameters) super().__init__(parameters=parameters, component_obj=en_regressor, random_seed=random_seed)
@property def feature_importance(self): return self._component_obj.coef_