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MAINT Use check_scalar in _BaseStacking #22405
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Merged
thomasjpfan
merged 11 commits into
scikit-learn:main
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genvalen:stacking_add_check_scalar
Apr 17, 2022
Merged
MAINT Use check_scalar in _BaseStacking #22405
thomasjpfan
merged 11 commits into
scikit-learn:main
from
genvalen:stacking_add_check_scalar
Apr 17, 2022
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…nto stacking_add_check_scalar
…nto stacking_add_check_scalar
Hi @glemaitre, please review this when you are able. Also FYI, based on a conversation with @thomasjpfan a few community sessions ago, I'm going to work on the splitter classes in a separate PR. |
glemaitre
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Mar 21, 2022
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
ogrisel
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ogrisel
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LGTM if CI is green.
thomasjpfan
approved these changes
Apr 17, 2022
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Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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2B80
Reference Issues/PRs
Addresses #20724 and #21927
#DataUmbrella
What does this implement/fix? Explain your changes.
Summary of changes to BaseGradientBoosting:
StackingClassifier
andStackingRegressor
raise proper errors when invalid arguments are passed in.
check_scalar
fromsklearn.utils
to validate the scalar parameters.Test and validation progress:
cv(cv & other splitter classes will be validated in a separate PR )verbose(per previous convos, this doesn't need to be validated for now -- I'll focus on hyperparams)Any other comments?
Please advise correct type/range if different than what I have below. (Thank you):
param: type-> range
cv
: int-> (2, inf]passthrough
: np.bool_, or int -> [-inf, inf]verbose
: int-> np.bool_, or [0, inf)