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FIX accept meta-estimator in SelfTrainingClassifier #19126

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Merged
merged 5 commits into from
Jan 8, 2021
Merged

FIX accept meta-estimator in SelfTrainingClassifier #19126

merged 5 commits into from
Jan 8, 2021

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glemaitre
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closes #19119

SelfTrainingClassifier did not accept nested estimators that did not expose predict_proba.
One fix is to validate a fitted estimator where we know if predict_proba will be then available.

glemaitre
glemaitre commented Jan 6, 2021
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sklearn/semi_supervised/_self_training.py Outdated
@@ -207,8 +207,11 @@ def fit(self, X, y):

if self.n_iter_ == 1:
# Only validate in the first iteration so that n_iter=0 is
# equivalent to the base_estimator itself.
_validate_estimator(self.base_estimator)
# equivalent to the base_estimator_ itself.
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@oliverrausch Do you recall what is the meaning of this line and the previous one?
It was not obvious to me.

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@ogrisel ogrisel Jan 6, 2021 •
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May I suggest:

# Only validate the fitted estimator of the first iteration.

Actually since the validation is really cheap, I would not mind simplifying this code and removing the if self.n_iter_ == 1: condition.

@glemaitre glemaitre added this to the 0.24.1 milestone Jan 6, 2021
@glemaitre glemaitre added the To backport PR merged in master that need a backport to a release branch defined based on the milestone. label Jan 6, 2021
thomasjpfan
thomasjpfan approved these changes Jan 6, 2021
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LGTM

@glemaitre @thomasjpfan
Update sklearn/semi_supervised/tests/test_self_training.py
a5c161f 
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
ogrisel
ogrisel approved these changes Jan 6, 2021
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LGTM (once the review comments are addressed).

sklearn/semi_supervised/_self_training.py Outdated
@@ -207,8 +207,11 @@ def fit(self, X, y):

if self.n_iter_ == 1:
# Only validate in the first iteration so that n_iter=0 is
# equivalent to the base_estimator itself.
_validate_estimator(self.base_estimator)
# equivalent to the base_estimator_ itself.
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@ogrisel ogrisel Jan 6, 2021 •
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May I suggest:

# Only validate the fitted estimator of the first iteration.

Actually since the validation is really cheap, I would not mind simplifying this code and removing the if self.n_iter_ == 1: condition.

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riyadhctg commented Jan 6, 2021 •
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LGTM. For the sake of sharing, the solution I had in my mind for #19119 was to change the order of the following two lines in https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py

names, all_estimators = self._validate_estimators()
self._validate_final_estimator()

to

self._validate_final_estimator()
names, all_estimators = self._validate_estimators()

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@riyadhctg The changes that you proposed is in the StackingClassifier. I think that the code is fine there. The changes required is in the SelfTrainingClassifier.

glemaitre and others added 2 commits January 6, 2021 17:29
@glemaitre @ogrisel
Update doc/whats_new/v0.24.rst
59f11a7 
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
@glemaitre
address ogrisel comments
d8728de
@ogrisel ogrisel merged commit 6d3d1b8 into scikit-learn:master Jan 8, 2021
glemaitre added a commit to glemaitre/scikit-learn that referenced this pull request Jan 18, 2021
@glemaitre @thomasjpfan @ogrisel
FIX accept meta-estimator in SelfTrainingClassifier (scikit-learn#19126)
59f6ec9 
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
jeremiedbb pushed a commit that referenced this pull request Jan 19, 2021
@glemaitre @thomasjpfan
@ogrisel @jeremiedbb
FIX accept meta-estimator in SelfTrainingClassifier (#19126)
78e1530 
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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StackClassifier is SelfTrainingClassifier throws error
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