PR welcome on stratified K-folds?

[SimonEnsemble]

I can make a first-round PR, then willing to make whatever changes necessary, if anyone is willing to coach me through it/ look at my PR. thx.

[darsnack]

Yes, this would be a nice addition. You can probably adapt the MLDataPattern.jl code as a starting point. I would try and start with the simplest implementation first though, since part of the goal with this package is to cut down on the complexity of the MLDataPattern.jl codebase.

[SimonEnsemble]

feel like we should start with the split observations, stratified version, first.

this is the simplest/clearest implementation I could come up with that works for (i) target vector y with any number of classes and (ii) an at that could be a tuple. is there a cleaner way to do this? also, these are not views like in the rest of the package...

will need some coaching... can write a few tests for this though.

function splitobs_stratified(;at, y::Array, shuffle::Bool=true)
	n_splits = length(at) + 1
	the_splits = [Int[] for s = 1:n_splits]
	for label in unique(y)
		ids_this_label = filter(i -> y[i] == label, 1:length(y))
		if shuffle
			ids_this_label = shuffleobs(ids_this_label)
		end
		split_this_label = splitobs(ids_this_label, at=at)
		for s = 1:n_splits
			the_splits[s] = vcat(the_splits[s], split_this_label[s])
		end
	end
	return the_splits
end

targets = vcat([-1 for i = 1:10], [1 for i = 1:100])
splits = splitobs_stratified(at=(0.2, 0.5), y=targets, shuffle=true)
for s in splits
       println(sum(targets[s] .== 1) / sum(targets[s] .== -1)) # 10.0 woo!
end

[CarloLucibello]

@SimonEnsemble your approach seems fine, you should open a PR, we can discuss there the details