Another advantage of the approach I proposed the other day is that its
overhead in sorting through the parameters is done once per search.
Solutions that integrate the planning into the fitting require the planning
to be done once per fold.
A big frustration in implementing any of this: grouping by parameter values
is complicated by the fact that parameter values may not be orderable,
boolean-comparable or hashable (numpy arrays are none of these), which
means the standard groupby(sorted(...)) and defaultdict(list) cannot be
used to bin them together. (This could be avoided if we had your original
proposal of receiving a set of values for each parameter; but we can't
really make the assumption that everything is a grid.)
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