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Implement __sklearn_is_fitted__. (#7230)

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Jiaming Yuan 2021-09-15 19:09:04 +08:00 committed by GitHub
parent d997c967d5
commit 037dd0820d
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2 changed files with 5 additions and 2 deletions
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5
python-package/xgboost/sklearn.py
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@ -435,6 +435,9 @@ class XGBModel(XGBModelBase):
'''Tags used for scikit-learn data validation.''' '''Tags used for scikit-learn data validation.'''
return {'allow_nan': True, 'no_validation': True} return {'allow_nan': True, 'no_validation': True}
def __sklearn_is_fitted__(self) -> bool:
return hasattr(self, "_Booster")
def get_booster(self) -> Booster: def get_booster(self) -> Booster:
"""Get the underlying xgboost Booster of this model. """Get the underlying xgboost Booster of this model.
@ -444,7 +447,7 @@ class XGBModel(XGBModelBase):
------- -------
booster : a xgboost booster of underlying model booster : a xgboost booster of underlying model
""" """
if not hasattr(self, '_Booster'): if not self.__sklearn_is_fitted__():
from sklearn.exceptions import NotFittedError from sklearn.exceptions import NotFittedError
raise NotFittedError('need to call fit or load_model beforehand') raise NotFittedError('need to call fit or load_model beforehand')
return self._Booster return self._Booster

2
tests/python-gpu/test_gpu_with_sklearn.py
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@ -19,7 +19,7 @@ def test_gpu_binary_classification():
from sklearn.datasets import load_digits from sklearn.datasets import load_digits
from sklearn.model_selection import KFold from sklearn.model_selection import KFold
digits = load_digits(2) digits = load_digits(n_class=2)
y = digits['target'] y = digits['target']
X = digits['data'] X = digits['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng) kf = KFold(n_splits=2, shuffle=True, random_state=rng)