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
[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=0.0001, l1_ratio=0.15, max_iter=1000, normalize=False, random_seed=0, **kwargs):
parameters = {'alpha': alpha,
'l1_ratio': l1_ratio,
'max_iter': max_iter,
'normalize': normalize}
parameters.update(kwargs)
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_