Merge pull request jump-dev#2179 from JuliaOpt/bl/empty_sparse

Fix construction of empty SparseAxisArray

src/Containers/container.jl

@@ -85,11 +85,22 @@ function container(f::Function, indices::VectorizedProductIterator,
     return DenseAxisArray(map(I -> f(I...), indices), indices.prod.iterators...)
 end
 default_container(::NestedIterator) = SparseAxisArray
+# Returns the element type. If it is unknown but it is known to be `N`-tuples,
+# returns `NTuple{N, Any}`.
+_eltype_or_any(indices::Array) = eltype(indices)
 function container(f::Function, indices,
                    ::Type{SparseAxisArray})
     # Same as `map` but does not allocate the resulting vector.
     mappings = Base.Generator(I -> I => f(I...), indices)
     # Same as `Dict(mapping)` but it will error if two indices are the same.
     data = NoDuplicateDict(mappings)
-    return SparseAxisArray(data.dict)
+    dict = data.dict
+    if isempty(dict)
+        # If `dict` is empty, it was not able to determine the type
+        # of the key hence the type of `dict` is `Dict{Any, Any}`.
+        # This is an issue since `SparseAxisArray` needs the key
+        # type to be a tuple.
+        dict = Dict{_eltype_or_any(indices), Any}()
+    end
+    return SparseAxisArray(dict)
 end

src/Containers/macro.jl

@@ -175,9 +175,9 @@ function container_code(idxvars, indices, code, requested_container)
         return :(error($error_message))
     end
     if requested_container == :DenseAxisArray
-        requested_container = :(JuMP.Containers.DenseAxisArray)
+        requested_container = :(Containers.DenseAxisArray)
     elseif requested_container == :SparseAxisArray
-        requested_container = :(JuMP.Containers.SparseAxisArray)
+        requested_container = :(Containers.SparseAxisArray)
     end
     esc_idxvars = esc.(idxvars)
     func = :(($(esc_idxvars...),) -> $code)

src/Containers/nested_iterator.jl

@@ -68,3 +68,4 @@ function tail_iterate(iterators, condition, elems, states, prev_states)
 end
 Base.iterate(it::NestedIterator) = first_iterate(it.iterators, it.condition, tuple(), tuple())
 Base.iterate(it::NestedIterator, states) = tail_iterate(it.iterators, it.condition, tuple(), states, tuple())
+_eltype_or_any(::NestedIterator{<:Tuple{Vararg{Any,N}}}) where N = NTuple{N, Any}

src/Containers/vectorized_product_iterator.jl

@@ -60,3 +60,4 @@ _prod_indices(t::Tuple) = (Base.OneTo(length(t[1])), _prod_indices(Base.tail(t))
 Base.ndims(it::VectorizedProductIterator) = length(axes(it))
 Base.length(it::VectorizedProductIterator) = prod(size(it))
 Base.iterate(it::VectorizedProductIterator, args...) = iterate(it.prod, args...)
+_eltype_or_any(it::VectorizedProductIterator) = eltype(it.prod)

test/Containers/macro.jl

@@ -26,5 +26,13 @@ using JuMP.Containers
         @test x isa Containers.SparseAxisArray{Int, 2}
         Containers.@container(x[i=1:10; iseven(i)], i)
         @test x isa Containers.SparseAxisArray{Int, 1}
+        Containers.@container(x[i=1:0, j=i:0], i)
+        @test x isa SparseAxisArray{Any,2,Tuple{Any,Any}}
+        Containers.@container(x[i=1:2, j=1:2; false], i)
+        @test x isa SparseAxisArray{Any,2,Tuple{Any,Any}}
+        Containers.@container(x[i=1:0, j=2:1], i, container = SparseAxisArray)
+        @test x isa SparseAxisArray{Any,2,Tuple{Int,Int}}
+        Containers.@container(x[i=1:0, j=1:0], i, container = SparseAxisArray)
+        @test x isa SparseAxisArray{Any,2,Tuple{Int,Int}}
     end
 end