cleanup

src/resample.jl

@@ -211,98 +211,3 @@ function undersample(rng::AbstractRNG, data::Tuple; kws...)
     d, c = undersample(rng, data[1:end-1], data[end]; kws...)
     return (d..., c)
 end
-
-
-"""
-    stratifiedobs([rng], data, p; [shuffle = true]) -> Tuple
-
-Partition the dataset `data` into multiple disjoint subsets 
-with size proportional to the value(s) of `p`. 
-The observations are assignmed to a data subset using stratified sampling without replacement. 
-
-If `p` is a float between 0 and 1, then the return value
-will be a tuple with two subsests in which the
-first element contains the fraction of observations specified by
-`p` and the second element contains the rest. In the following
-code the first subset `train` will contain around 70% of the
-observations and the second subset `test` the rest. The key
-difference to [`splitobs`](@ref) is that the class distribution
-in `y` will actively be preserved in `train` and `test`.
-
-```julia
-train_data, test_data = stratifiedobs(data, p = 0.7)
-```
-
-If `p` is a tuple of floats between 0 and 1, then additional subsets will be
-created. In this example `train` will contain about 50% of the
-observations, `val` will contain around 30%, and `test` the
-remaining 20%.
-
-```julia
-train_data, val_data, test_data = stratifiedobs(y, p = (0.5, 0.3))
-```
-
-It is also possible to call `stratifiedobs` with multiple data
-arguments as tuple, which all must have the same number of total
-observations. Note that if `data` is a tuple, then it will be
-assumed that the last element of the tuple contains the targets.
-
-```julia
-(X_train, y_train), (X_test, y_test) = stratifiedobs((X, y), p = 0.7)
-```
-
-The optional parameter `shuffle` determines if the resulting data
-subsets should be shuffled. If `false`, then the observations in
-the subsets will be grouped together according to their labels.
-
-```julia
-julia> y = ["a", "b", "b", "b", "b", "a"] # 2 imbalanced classes
-6-element Array{String,1}:
- "a"
- "b"
- "b"
- "b"
- "b"
- "a"
-
-julia> train, test = stratifiedobs(y, p = 0.5, shuffle = false)
-(String["b","b","a"],String["b","b","a"])
-```
-
-The optional argument `rng` allows one to specify the
-random number generator used for shuffling. 
-
-For this function to work, the type of `data` must implement
-[`numobs`](@ref) and [`getobs`](@ref). 
-
-See also [`undersample`](@ref), [`oversample`](@ref), and [`splitobs`](@ref).
-"""
-function stratifiedobs(data; p = 0.7, shuffle = true, obsdim = default_obsdim(data), rng = Random.GLOBAL_RNG)
-    stratifiedobs(identity, data, p, shuffle, convert(ObsDimension, obsdim), rng)
-end
-
-function stratifiedobs(f, data; p = 0.7, shuffle = true, obsdim = default_obsdim(data), rng = Random.GLOBAL_RNG)
-    stratifiedobs(f, data, p, shuffle, convert(ObsDimension, obsdim), rng)
-end
-
-function stratifiedobs(data, p::AbstractFloat, args...)
-    stratifiedobs(identity, data, p, args...)
-end
-
-function stratifiedobs(data, p::NTuple{N,AbstractFloat}, args...) where N
-    stratifiedobs(identity, data, p, args...)
-end
-
-function stratifiedobs(rng, data, p::Union{NTuple,AbstractFloat}, stratified::AbstractVector)
-    # The given data is always shuffled to qualify as performing
-    # stratified sampling without replacement.
-    idxs_groups = group_indices(stratified)
-    idxs_splits = ntuple(i -> Int[], length(p)+1)
-    for (lbl, idxs) in idxs_groups
-        new_idxs_splits = splitobs(rng, idxs, at=p)
-        for i in 1:length(idxs_splits)
-            append!(idxs_splits[i], new_idxs_splits[i])
-        end
-    end
-    return map(idx -> obsview(data, idx), idxs_splits)
-end