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Hi,
this is a bit connected to accepting sparse matrices - basically about sklearn validations.
I understand why not accepting NaN is sometimes necessary for some algorithms, it doesn't make sense for e.g. *RandomSamplers. NaNs could be copied as well.
As a workaround, I replace all NaNs by some other valid value and then convert it back. But it is of course a bit weird...