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]
[docs] def __init__(self, alpha=0.5, l1_ratio=0.5, random_state=0, normalize=False,
max_iter=1000, n_jobs=-1):
parameters = {'alpha': alpha,
'l1_ratio': l1_ratio}
en_regressor = SKElasticNet(alpha=alpha,
l1_ratio=l1_ratio,
random_state=random_state,
normalize=normalize,
max_iter=max_iter
)
super().__init__(parameters=parameters,
component_obj=en_regressor,
random_state=random_state)
@property
def feature_importances(self):
return self._component_obj.coef_