Handle SparseArrays.jl dep as a Pkg extension (#251)

Co-authored-by: David Widmann <[email protected]>

Project.toml

@@ -1,22 +1,29 @@
 name = "Distances"
 uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-version = "0.10.8"
+version = "0.10.9"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 
+[weakdeps]
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+
+[extensions]
+DistancesSparseArraysExt = "SparseArrays"
+
 [compat]
 StatsAPI = "1"
 julia = "1"
 
 [extras]
 OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [targets]
-test = ["OffsetArrays", "Random", "Test", "Unitful"]
+test = ["OffsetArrays", "Random", "SparseArrays", "Test", "Unitful"]

ext/DistancesSparseArraysExt.jl

@@ -0,0 +1,83 @@
+module DistancesSparseArraysExt
+
+using Distances
+import Distances: _evaluate
+using Distances: UnionMetrics, result_type, eval_start, eval_op, eval_reduce, eval_end
+using SparseArrays: SparseVectorUnion, nonzeroinds, nonzeros, nnz
+using SparseArrays: SparseVectorUnion
+
+eval_op_a(d, ai, b) = eval_op(d, ai, zero(eltype(b)))
+eval_op_b(d, bi, a) = eval_op(d, zero(eltype(a)), bi)
+
+# It is assumed that eval_reduce(d, s, eval_op(d, zero(eltype(a)), zero(eltype(b)))) == s
+# This justifies ignoring all terms where both inputs are zero.
+Base.@propagate_inbounds function _evaluate(d::UnionMetrics, a::SparseVectorUnion, b::SparseVectorUnion, ::Nothing)
+    @boundscheck if length(a) != length(b)
+        throw(DimensionMismatch("first array has length $(length(a)) which does not match the length of the second, $(length(b))."))
+    end
+    if length(a) == 0
+        return zero(result_type(d, a, b))
+    end
+    anzind = nonzeroinds(a)
+    bnzind = nonzeroinds(b)
+    anzval = nonzeros(a)
+    bnzval = nonzeros(b)
+    ma = nnz(a)
+    mb = nnz(b)
+    ia = 1; ib = 1
+    s = eval_start(d, a, b)
+    @inbounds while ia <= ma && ib <= mb
+        ja = anzind[ia]
+        jb = bnzind[ib]
+        if ja == jb
+            v = eval_op(d, anzval[ia], bnzval[ib])
+            ia += 1; ib += 1
+        elseif ja < jb
+            v = eval_op_a(d, anzval[ia], b)
+            ia += 1
+        else
+            v = eval_op_b(d, bnzval[ib], a)
+            ib += 1
+        end
+        s = eval_reduce(d, s, v)
+    end
+    @inbounds while ia <= ma
+        v = eval_op_a(d, anzval[ia], b)
+        s = eval_reduce(d, s, v)
+        ia += 1
+    end
+    @inbounds while ib <= mb
+        v = eval_op_b(d, bnzval[ib], a)
+        s = eval_reduce(d, s, v)
+        ib += 1
+    end
+    return eval_end(d, s)
+end
+
+@inline function _bhattacharyya_coeff(a::SparseVectorUnion, b::SparseVectorUnion)
+    anzind = nonzeroinds(a)
+    bnzind = nonzeroinds(b)
+    anzval = nonzeros(a)
+    bnzval = nonzeros(b)
+    ma = nnz(a)
+    mb = nnz(b)
+
+    ia = 1; ib = 1
+    s = zero(typeof(sqrt(oneunit(eltype(a))*oneunit(eltype(b)))))
+    @inbounds while ia <= ma && ib <= mb
+        ja = anzind[ia]
+        jb = bnzind[ib]
+        if ja == jb
+            s += sqrt(anzval[ia] * bnzval[ib])
+            ia += 1; ib += 1
+        elseif ja < jb
+            ia += 1
+        else
+            ib += 1
+        end
+    end
+    # efficient method for sum for SparseVectorView is missing
+    return s, sum(anzval), sum(bnzval)
+end
+
+end # module DistancesSparseArraysExt

src/Distances.jl

@@ -1,8 +1,7 @@
 module Distances
 
 using LinearAlgebra
-using Statistics
-using SparseArrays: SparseVectorUnion, nonzeroinds, nonzeros, nnz
+using Statistics: mean
 import StatsAPI: pairwise, pairwise!
 
 export
@@ -120,4 +119,8 @@ include("bregman.jl")
 
 include("deprecated.jl")
 
+@static if !isdefined(Base, :get_extension)
+    include("../ext/DistancesSparseArraysExt.jl")
+end
+
 end # module end

src/bhattacharyya.jl

@@ -50,32 +50,6 @@ end
     return sqab, asum, bsum
 end
 
-@inline function _bhattacharyya_coeff(a::SparseVectorUnion, b::SparseVectorUnion)
-    anzind = nonzeroinds(a)
-    bnzind = nonzeroinds(b)
-    anzval = nonzeros(a)
-    bnzval = nonzeros(b)
-    ma = nnz(a)
-    mb = nnz(b)
-
-    ia = 1; ib = 1
-    s = zero(typeof(sqrt(oneunit(eltype(a))*oneunit(eltype(b)))))
-    @inbounds while ia <= ma && ib <= mb
-        ja = anzind[ia]
-        jb = bnzind[ib]
-        if ja == jb
-            s += sqrt(anzval[ia] * bnzval[ib])
-            ia += 1; ib += 1
-        elseif ja < jb
-            ia += 1
-        else
-            ib += 1
-        end
-    end
-    # efficient method for sum for SparseVectorView is missing
-    return s, sum(anzval), sum(bnzval)
-end
-
 # Faster pair- and column-wise versions TBD...
 
 

src/metrics.jl

@@ -308,55 +308,6 @@ Base.@propagate_inbounds function _evaluate(d::UnionMetrics, a::AbstractArray, b
     end
 end
 
-eval_op_a(d, ai, b) = eval_op(d, ai, zero(eltype(b)))
-eval_op_b(d, bi, a) = eval_op(d, zero(eltype(a)), bi)
-
-# It is assumed that eval_reduce(d, s, eval_op(d, zero(eltype(a)), zero(eltype(b)))) == s
-# This justifies ignoring all terms where both inputs are zero.
-Base.@propagate_inbounds function _evaluate(d::UnionMetrics, a::SparseVectorUnion, b::SparseVectorUnion, ::Nothing)
-    @boundscheck if length(a) != length(b)
-        throw(DimensionMismatch("first array has length $(length(a)) which does not match the length of the second, $(length(b))."))
-    end
-    if length(a) == 0
-        return zero(result_type(d, a, b))
-    end
-    anzind = nonzeroinds(a)
-    bnzind = nonzeroinds(b)
-    anzval = nonzeros(a)
-    bnzval = nonzeros(b)
-    ma = nnz(a)
-    mb = nnz(b)
-    ia = 1; ib = 1
-    s = eval_start(d, a, b)
-    @inbounds while ia <= ma && ib <= mb
-        ja = anzind[ia]
-        jb = bnzind[ib]
-        if ja == jb
-            v = eval_op(d, anzval[ia], bnzval[ib])
-            ia += 1; ib += 1
-        elseif ja < jb
-            v = eval_op_a(d, anzval[ia], b)
-            ia += 1
-        else
-            v = eval_op_b(d, bnzval[ib], a)
-            ib += 1
-        end
-        s = eval_reduce(d, s, v)
-    end
-    @inbounds while ia <= ma
-        v = eval_op_a(d, anzval[ia], b)
-        s = eval_reduce(d, s, v)
-        ia += 1
-    end
-    @inbounds while ib <= mb
-        v = eval_op_b(d, bnzval[ib], a)
-        s = eval_reduce(d, s, v)
-        ib += 1
-    end
-    return eval_end(d, s)
-end
-
-
 _evaluate(dist::UnionMetrics, a::Number, b::Number, ::Nothing) = eval_end(dist, eval_op(dist, a, b))
 function _evaluate(dist::UnionMetrics, a::Number, b::Number, p)
     length(p) != 1 && throw(DimensionMismatch("inputs are scalars but parameters have length $(length(p))."))