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In imbalanced-learn we have cases (e.g. scikit-learn-contrib/imbalanced-learn#157) where some samplers like RandomUnderSampler and RandomOverSampler could naturally accept NaNs for X because the values of the X are irrelevant to the random sampling itself. If we modifiy the Random*Samplers to accept NaNs the check_estimator tests fall for them.
So we have two options:
- Skip the
check_estimatortests for theRandom*Samplers - Do not accept the
NaNsat all and document it clearly.
Any workaround on this? What are your thoughts?
This issue in general is related to #6981.
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