Map over combinations of parameters, and grouping results as DataFrame

General Usage
10

I’ve noticed that reduce has an optional source argument specifically to keep track of each block. With this, I now have the following wrapper:


function expand_grid(; kws...)
    names, vals = keys(kws), values(kws)
    return DataFrame(NamedTuple{names}(t) for t in Iterators.product(vals...))
end

function mymodel(;x = range(0, 10, length = 100), a = 1, b = 1, c = 2, fun = sin, kws...)
    DataFrame(x = x, y = a .* sin.(b .* x) .+ c, z = a .* fun.(b .* x) .+ c)
end

function pmap_df(p, f, kws...; join=true)
   tmp = map(f, eachrow(p), kws...)
   all = reduce(vcat, tmp, source="id")
   if !join
    return all
   end
   p[!, :id] = 1:nrow(p)
   return DataFrames.leftjoin(p, all, on=:id)
end

params = expand_grid(a=[0.1,0.2,0.3], b = [1,2,3], c = [0,0.5]);

pmap_df(params, p -> mymodel(;p..., fun=tanh))

That’s pretty close to what I was after; I’d still like to merge the map+reduce steps with mapreduce, but I’m not sure how to pass extra arguments to reduce. Any idea?