Source code for evalml.pipelines.components.estimators.classifiers.elasticnet_classifier

import warnings

import numpy as np
from sklearn.linear_model import LogisticRegression
from skopt.space import Real

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes


class ElasticNetClassifier(Estimator):
    """
    Elastic Net Classifier. Uses Logistic Regression with elasticnet penalty as the base estimator.
    """

    name = "Elastic Net Classifier"
    hyperparameter_ranges = {"C": Real(0.01, 10), "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,
    ]

    def __init__(
        self,
        penalty="elasticnet",
        C=1.0,
        l1_ratio=0.15,
        n_jobs=-1,
        multi_class="auto",
        solver="saga",
        random_seed=0,
        **kwargs
    ):
        parameters = {
            "penalty": penalty,
            "C": C,
            "l1_ratio": l1_ratio,
            "n_jobs": n_jobs,
            "multi_class": multi_class,
            "solver": solver,
        }
        parameters.update(kwargs)
        lr_classifier = LogisticRegression(random_state=random_seed, **parameters)
        super().__init__(
            parameters=parameters, component_obj=lr_classifier, random_seed=random_seed
        )

    def fit(self, X, y):
        warnings.filterwarnings("ignore", message="The max_iter was reached")
        return super().fit(X, y)

    @property
    def feature_importance(self):
        coef_ = self._component_obj.coef_
        # binary classification case
        if len(coef_) <= 2:
            return coef_[0]
        else:
            # multiclass classification case
            return np.linalg.norm(coef_, axis=0, ord=2)