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

"""Elastic Net Regressor."""
import pandas as pd
from sklearn.linear_model import ElasticNet as SKElasticNet
from 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. Args: 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. 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, random_seed=0, **kwargs, ): parameters = { "alpha": alpha, "l1_ratio": l1_ratio, "max_iter": max_iter, } 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): """Feature importance for fitted ElasticNet regressor.""" return pd.Series(self._component_obj.coef_)