import warnings
import numpy as np
from sklearn.linear_model import SGDClassifier as SKElasticNetClassifier
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 ElasticNetClassifier(Estimator):
"""Elastic Net Classifier."""
name = "Elastic Net Classifier"
hyperparameter_ranges = {
"alpha": Real(0, 1),
"l1_ratio": Real(0, 1),
}
model_family = ModelFamily.LINEAR_MODEL
supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS,
ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS]
[docs] def __init__(self, alpha=0.5, l1_ratio=0.5, n_jobs=-1, max_iter=1000, random_state=None,
random_seed=0, penalty='elasticnet',
**kwargs):
parameters = {'alpha': alpha,
'l1_ratio': l1_ratio,
'n_jobs': n_jobs,
'max_iter': max_iter,
'penalty': penalty}
if kwargs.get('loss', 'log') != 'log':
warnings.warn("Parameter loss is being set to 'log' so that ElasticNetClassifier can predict probabilities"
f". Originally received '{kwargs['loss']}'.")
kwargs["loss"] = "log"
parameters.update(kwargs)
random_seed = deprecate_arg("random_state", "random_seed", random_state, random_seed)
en_classifier = SKElasticNetClassifier(random_state=random_seed,
**parameters)
super().__init__(parameters=parameters,
component_obj=en_classifier,
random_seed=random_seed)
@property
def feature_importance(self):
coef_ = self._component_obj.coef_
# binary classification case
if len(coef_) <= 2:
return coef_.flatten()
else:
# multiclass classification case
return np.linalg.norm(coef_, axis=0, ord=2)