update reduce to 1.0-style keyword args

Author: JeffBezanson

benchmark/benchmarks.jl

@@ -87,8 +87,8 @@ let
         b = rand(0:0.001:2, N)
         c = rand(N)
         t4 = IndexedTable(a, b, c)
-        @bench "dim-1" reducedim(+, $t4, 1)
-        @bench "dim-2" reducedim(+, $t4, 2)
+        @bench "dim-1" reduce(+, $t4, dims=1)
+        @bench "dim-2" reduce(+, $t4, dims=2)
         @bench "vec-dim-1" reducedim_vec(+, $t4, 1)
         @bench "vec-dim-2" reducedim_vec(+, $t4, 2)
     end

src/reduce.jl

@@ -101,19 +101,24 @@ julia> reduce(@NT(xsum=:x=>+, negtsum=(:t=>-)=>+), t)
 See [`Selection`](@ref) for more on what selectors can be specified. Here since each output can select its own input, `select` keyword is unsually unnecessary. If specified, the slections in the reducer tuple will be done over the result of selecting with the `select` argument.
 
 """
-function reduce(f, t::Dataset; select=valuenames(t))
+function reduce(f, t::NextTable; select=valuenames(t), kws...)
+    if haskey(kws, :init)
+        return _reduce_select_init(f, t, select, kws.data.init)
+    end
+    _reduce_select(f, t, select)
+end
+
+function _reduce_select(f, t::Dataset, select)
     fs, input, T = init_inputs(f, rows(t, select), reduced_type, false)
     acc = init_first(fs, input[1])
     _reduce(fs, input, acc, 2)
 end
 
-function reduce(f, v0, t::Dataset; select=valuenames(t))
+function _reduce_select_init(f, t::Dataset, select, v0)
     fs, input, T = init_inputs(f, rows(t, select), reduced_type, false)
     _reduce(fs, input, v0, 1)
 end
 
-@deprecate reduce(f, t::Dataset, v0; select=valuenames(t)) reduce(f, v0, t::Dataset; select=select)
-
 function _reduce(fs, input, acc, start)
     @inbounds @simd for i=start:length(input)
         acc = _apply(fs, acc, input[i])
@@ -596,16 +601,29 @@ y │
 
 ```
 """
-function Base.reduce(f, x::NDSparse, dims)
-    keep = setdiff([1:ndims(x);], map(d->fieldindex(x.index.columns,d), dims))
-    if isempty(keep)
-        throw(ArgumentError("to remove all dimensions, use `reduce(f, A)`"))
+function Base.reduce(f, x::NDSparse; kws...)
+    if haskey(kws, :dims)
+        if haskey(kws, :select) || haskey(kws, :init)
+            throw(ArgumentError("select and init keyword arguments cannot be used with dims"))
+        end
+        dims = kws.data.dims
+        if dims isa Symbol
+            dims = [dims]
+        end
+        keep = setdiff([1:ndims(x);], map(d->fieldindex(x.index.columns,d), dims))
+        if isempty(keep)
+            throw(ArgumentError("to remove all dimensions, use `reduce(f, A)`"))
+        end
+        return groupreduce(f, x, (keep...,))
+    else
+        select = get(kws, :select, valuenames(x))
+        if haskey(kws, :init)
+            return _reduce_select_init(f, x, select, kws.data.init)
+        end
+        return _reduce_select(f, x, select)
     end
-    groupreduce(f, x, (keep...,))
 end
 
-Base.reduce(f, x::NDSparse, dims::Symbol) = reduce(f, x, [dims])
-
 """
 `reducedim_vec(f::Function, arr::NDSparse, dims)`
 

test/test_core.jl

@@ -178,14 +178,14 @@ let a = rand(5,5,5)
     for dims in ([2,3], [1], [2])
         r = dropdims(reduce(+, a; dims=dims), dims=(dims...,))
         asnd = convert(NDSparse,a)
-        b = reduce(+, asnd, dims)
+        b = reduce(+, asnd, dims=dims)
         bv = reducedim_vec(sum, asnd, dims)
         c = convert(NDSparse, r)
         @test b.index == c.index == bv.index
         @test b.data ≈ c.data
         @test bv.data ≈ c.data
     end
-    @test_throws ArgumentError reduce(+, convert(NDSparse,a), [1,2,3])
+    @test_throws ArgumentError reduce(+, convert(NDSparse,a), dims=[1,2,3])
 end
 
 for a in (rand(2,2), rand(3,5))
@@ -727,8 +727,8 @@ end
 
 @testset "reducedim" begin
     x = ndsparse((x = [1, 1, 1, 2, 2, 2], y = [1, 2, 2, 1, 2, 2], z = [1, 1, 2, 1, 1, 2]), [1, 2, 3, 4, 5, 6])
-    @test reduce(+, x, 1) == ndsparse((y = [1, 2, 2], z = [1, 1, 2]), [5, 7, 9])
-    @test reduce(+, x, (1, 3)) == ndsparse((y = [1, 2],), [5, 16])
+    @test reduce(+, x, dims=1) == ndsparse((y = [1, 2, 2], z = [1, 1, 2]), [5, 7, 9])
+    @test reduce(+, x, dims=(1, 3)) == ndsparse((y = [1, 2],), [5, 16])
 end
 
 @testset "select" begin
@@ -883,7 +883,7 @@ using OnlineStats
 
     t = table([0.1, 0.5, 0.75], [0, 1, 2], names=[:t, :x])
     @test reduce(+, t, select=:t) == 1.35
-    @test reduce(+, 1.0, t, select = :t) == 2.35
+    @test reduce(+, t, init = 1.0, select = :t) == 2.35
     @test reduce(((a, b)->(t = a.t + b.t, x = a.x + b.x)), t) == (t = 1.35, x = 3)
     @test value(reduce(Mean(), t, select=:t)) == 0.45
     y = reduce((min, max), t, select=:x)