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

import numpy as np
from sklearn.linear_model import LogisticRegression as 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
from evalml.utils import deprecate_arg


[docs]class LogisticRegressionClassifier(Estimator): """ Logistic Regression Classifier. """ name = "Logistic Regression Classifier" hyperparameter_ranges = { "penalty": ["l2"], "C": Real(.01, 10), } model_family = ModelFamily.LINEAR_MODEL supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS]
[docs] def __init__(self, penalty="l2", C=1.0, n_jobs=-1, multi_class="auto", solver="lbfgs", random_state=None, random_seed=0, **kwargs): parameters = {"penalty": penalty, "C": C, "n_jobs": n_jobs, "multi_class": multi_class, "solver": solver} parameters.update(kwargs) random_seed = deprecate_arg("random_state", "random_seed", random_state, random_seed) lr_classifier = LogisticRegression(random_state=random_seed, **parameters) super().__init__(parameters=parameters, component_obj=lr_classifier, random_seed=random_seed)
@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)