API Freezing estimators

TODO: motivate freezing: pipeline components, calibration, transfer/semisupervised learning

This should probably be a SLEP, but I just want it saved somewhere.

Features required for estimator freezing:

clone must have is isinstance(obj, FrozenModel): return obj (or do so via class polymorphism / singledispatch)
FrozenModel delegates all attribute access (get, set, del) to its wrapped estimator (except where specified)
- hence its estimator cannot be accessible at FrozenModel().estimator but at some more munged name.
FrozenModel has def fit(self, *args, **kwargs): return self
FrozenModel has def fit_transform(self, *args, **kwargs): return fit(self, *args, **kwargs).transform(self, args[0]) (and similar for fit_predict?)
isinstance(freeze(obj), type(obj)) == True and isinstance(freeze(obj), FrozenModel) == True
- since this is determined from type(freeze(obj)) (excluding __instancecheck__, which seems irrelevant), this appears to be the hardest criterion to fulfill
- seems to entail use of a mixin, class created in closure, setting __class__ (!), overloading of __reduce__, help! I think I've gone down the wrong path!!
must behave nicely with pickle and copy.[deep]copy
freeze(some_list) will freeze every element of the list

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