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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
from sklearn.utils.validation import (
indexable,
_check_method_params,
check_is_fitted,
)
# NOTE Implementations of following methods from:
# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/model_selection/_search.py
# Tag: 1.5.1
def _check_refit(search_cv, attr):
if not search_cv.refit:
raise AttributeError(
f"This {type(search_cv).__name__} instance was initialized with "
f"`refit=False`. {attr} is available only after refitting on the best "
"parameters. You can refit an estimator manually using the "
"`best_params_` attribute"
def _estimator_has(attr):
def check(self):
_check_refit(self, attr)
if hasattr(self, "best_estimator_"):
# raise an AttributeError if `attr` does not exist
getattr(self.best_estimator_, attr)
return True
getattr(self.estimator, attr)
return check
SimonBlanke /
Hyperactive
