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[python][sklearn] add __sklearn_is_fitted__() method to be better compatible with scikit-learn API #4636

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Oct 5, 2021
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[python][sklearn] add __sklearn_is_fitted__() method to be better compatible with scikit-learn API #4636

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add `__sklearn_is_fitted__()` method to be better compatible with sci…
StrikerRUS Sep 30, 2021
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Merge branch 'master' into sk_is_fitted
StrikerRUS Oct 4, 2021
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3 changes: 3 additions & 0 deletions python-package/lightgbm/sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -529,6 +529,9 @@ def _more_tags(self):
}
}

def __sklearn_is_fitted__(self) -> bool:
return getattr(self, "fitted_", False)
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We set fitted_ attribute at the end of the fit() method

LightGBM/python-package/lightgbm/sklearn.py

Line 770 in a77260f

self.fitted_ = True

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@jameslamb jameslamb Oct 2, 2021 •
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I noticed in the dmlc/xgboost#7230, they also replaced some other internal checks of estimator attributes with this method call.

I think that's a good idea, so only this method needs to know about the fitted_ property. What do you think?

That would mean changing the following to if not self.__sklearn_is_fitted__():

LightGBM/python-package/lightgbm/dask.py

Lines 1004 to 1005 in a77260f

if not getattr(self, "fitted_", False):
raise LGBMNotFittedError('Cannot access property client_ before calling fit().')

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I agree. I remember you were asking why do we use if self._n_features is None everywhere for checking that estimator is fitted when we have self.fitted_.

LightGBM/python-package/lightgbm/sklearn.py

Line 857 in a77260f

if self._n_features is None:

#3883 (comment)
My answer was that it's because self.fitted_ was introduced much later. Now we can replace everything with self.__sklearn_is_fitted__(). And I think it will be clearer.

But I'd prefer to do it in a follow-up PR, if you do not mind.

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ok yep, I'm fine with it being a followup


def get_params(self, deep=True):
"""Get parameters for this estimator.

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