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.
Arguments:
alpha (float): Constant that multiplies the penalty terms. Defaults to 0.0001.
l1_ratio (float): The mixing parameter, with 0 <= l1_ratio <= 1. Only used if penalty='elasticnet'. Setting l1_ratio=0 is equivalent to using penalty='l2',
while setting l1_ratio=1 is equivalent to using penalty='l1'. For 0 < l1_ratio <1, the penalty is a combination of L1 and L2. Defaults to 0.15.
max_iter (int): The maximum number of iterations. Defaults to 1000.
normalize (boolean): If True, the regressors will be normalized before regression by subtracting the mean
and dividing by the l2-norm. Defaults to False.
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
name = "Elastic Net Regressor"
hyperparameter_ranges = {
"alpha": Real(0, 1),
"l1_ratio": Real(0, 1),
}
"""{
"alpha": Real(0, 1),
"l1_ratio": Real(0, 1),
}"""
model_family = ModelFamily.LINEAR_MODEL
"""ModelFamily.LINEAR_MODEL"""
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]"""
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_