make SparseArrays a weak dependency

Author: IanButterworth

Project.toml

@@ -7,11 +7,17 @@ version = "1.9.0"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+
+[gluedeps]
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
+[gluepkgs]
+SparseArraysGlue = ["SparseArrays"]
+
 [extras]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Random", "Test"]
+test = ["Random", "SparseArrays", "Test"]

glue/SparseArraysGlue.jl

@@ -0,0 +1,101 @@
+module SparseArraysGlue
+
+##### SparseArrays optimizations #####
+
+using Base: require_one_based_indexing
+using LinearAlgebra
+using SparseArrays
+using Statistics
+using Statistics: centralize_sumabs2, unscaled_covzm
+
+# extended functions
+import Statistics: cov, centralize_sumabs2!
+
+function cov(X::SparseMatrixCSC; dims::Int=1, corrected::Bool=true)
+    vardim = dims
+    a, b = size(X)
+    n, p = vardim == 1 ? (a, b) : (b, a)
+
+    # The covariance can be decomposed into two terms
+    # 1/(n - 1) ∑ (x_i - x̄)*(x_i - x̄)' = 1/(n - 1) (∑ x_i*x_i' - n*x̄*x̄')
+    # which can be evaluated via a sparse matrix-matrix product
+
+    # Compute ∑ x_i*x_i' = X'X using sparse matrix-matrix product
+    out = Matrix(unscaled_covzm(X, vardim))
+
+    # Compute x̄
+    x̄ᵀ = mean(X, dims=vardim)
+
+    # Subtract n*x̄*x̄' from X'X
+    @inbounds for j in 1:p, i in 1:p
+        out[i,j] -= x̄ᵀ[i] * x̄ᵀ[j]' * n
+    end
+
+    # scale with the sample size n or the corrected sample size n - 1
+    return rmul!(out, inv(n - corrected))
+end
+
+# This is the function that does the reduction underlying var/std
+function centralize_sumabs2!(R::AbstractArray{S}, A::SparseMatrixCSC{Tv,Ti}, means::AbstractArray) where {S,Tv,Ti}
+    require_one_based_indexing(R, A, means)
+    lsiz = Base.check_reducedims(R,A)
+    for i in 1:max(ndims(R), ndims(means))
+        if axes(means, i) != axes(R, i)
+            throw(DimensionMismatch("dimension $i of `mean` should have indices $(axes(R, i)), but got $(axes(means, i))"))
+        end
+    end
+    isempty(R) || fill!(R, zero(S))
+    isempty(A) && return R
+
+    rowval = rowvals(A)
+    nzval = nonzeros(A)
+    m = size(A, 1)
+    n = size(A, 2)
+
+    if size(R, 1) == size(R, 2) == 1
+        # Reduction along both columns and rows
+        R[1, 1] = centralize_sumabs2(A, means[1])
+    elseif size(R, 1) == 1
+        # Reduction along rows
+        @inbounds for col = 1:n
+            mu = means[col]
+            r = convert(S, (m - length(nzrange(A, col)))*abs2(mu))
+            @simd for j = nzrange(A, col)
+                r += abs2(nzval[j] - mu)
+            end
+            R[1, col] = r
+        end
+    elseif size(R, 2) == 1
+        # Reduction along columns
+        rownz = fill(convert(Ti, n), m)
+        @inbounds for col = 1:n
+            @simd for j = nzrange(A, col)
+                row = rowval[j]
+                R[row, 1] += abs2(nzval[j] - means[row])
+                rownz[row] -= 1
+            end
+        end
+        for i = 1:m
+            R[i, 1] += rownz[i]*abs2(means[i])
+        end
+    else
+        # Reduction along a dimension > 2
+        @inbounds for col = 1:n
+            lastrow = 0
+            @simd for j = nzrange(A, col)
+                row = rowval[j]
+                for i = lastrow+1:row-1
+                    R[i, col] = abs2(means[i, col])
+                end
+                R[row, col] = abs2(nzval[j] - means[row, col])
+                lastrow = row
+            end
+            for i = lastrow+1:m
+                R[i, col] = abs2(means[i, col])
+            end
+        end
+    end
+    return R
+end
+
+end # module

src/Statistics.jl

@@ -7,7 +7,7 @@ Standard library module for basic statistics functionality.
 """
 module Statistics
 
-using LinearAlgebra, SparseArrays
+using LinearAlgebra
 
 using Base: has_offset_axes, require_one_based_indexing
 
@@ -1073,94 +1073,4 @@ quantile(itr, p; sorted::Bool=false, alpha::Real=1.0, beta::Real=alpha) =
 quantile(v::AbstractVector, p; sorted::Bool=false, alpha::Real=1.0, beta::Real=alpha) =
     quantile!(sorted ? v : Base.copymutable(v), p; sorted=sorted, alpha=alpha, beta=beta)
 
-
-##### SparseArrays optimizations #####
-
-function cov(X::SparseMatrixCSC; dims::Int=1, corrected::Bool=true)
-    vardim = dims
-    a, b = size(X)
-    n, p = vardim == 1 ? (a, b) : (b, a)
-
-    # The covariance can be decomposed into two terms
-    # 1/(n - 1) ∑ (x_i - x̄)*(x_i - x̄)' = 1/(n - 1) (∑ x_i*x_i' - n*x̄*x̄')
-    # which can be evaluated via a sparse matrix-matrix product
-
-    # Compute ∑ x_i*x_i' = X'X using sparse matrix-matrix product
-    out = Matrix(unscaled_covzm(X, vardim))
-
-    # Compute x̄
-    x̄ᵀ = mean(X, dims=vardim)
-
-    # Subtract n*x̄*x̄' from X'X
-    @inbounds for j in 1:p, i in 1:p
-        out[i,j] -= x̄ᵀ[i] * x̄ᵀ[j]' * n
-    end
-
-    # scale with the sample size n or the corrected sample size n - 1
-    return rmul!(out, inv(n - corrected))
-end
-
-# This is the function that does the reduction underlying var/std
-function centralize_sumabs2!(R::AbstractArray{S}, A::SparseMatrixCSC{Tv,Ti}, means::AbstractArray) where {S,Tv,Ti}
-    require_one_based_indexing(R, A, means)
-    lsiz = Base.check_reducedims(R,A)
-    for i in 1:max(ndims(R), ndims(means))
-        if axes(means, i) != axes(R, i)
-            throw(DimensionMismatch("dimension $i of `mean` should have indices $(axes(R, i)), but got $(axes(means, i))"))
-        end
-    end
-    isempty(R) || fill!(R, zero(S))
-    isempty(A) && return R
-
-    rowval = rowvals(A)
-    nzval = nonzeros(A)
-    m = size(A, 1)
-    n = size(A, 2)
-
-    if size(R, 1) == size(R, 2) == 1
-        # Reduction along both columns and rows
-        R[1, 1] = centralize_sumabs2(A, means[1])
-    elseif size(R, 1) == 1
-        # Reduction along rows
-        @inbounds for col = 1:n
-            mu = means[col]
-            r = convert(S, (m - length(nzrange(A, col)))*abs2(mu))
-            @simd for j = nzrange(A, col)
-                r += abs2(nzval[j] - mu)
-            end
-            R[1, col] = r
-        end
-    elseif size(R, 2) == 1
-        # Reduction along columns
-        rownz = fill(convert(Ti, n), m)
-        @inbounds for col = 1:n
-            @simd for j = nzrange(A, col)
-                row = rowval[j]
-                R[row, 1] += abs2(nzval[j] - means[row])
-                rownz[row] -= 1
-            end
-        end
-        for i = 1:m
-            R[i, 1] += rownz[i]*abs2(means[i])
-        end
-    else
-        # Reduction along a dimension > 2
-        @inbounds for col = 1:n
-            lastrow = 0
-            @simd for j = nzrange(A, col)
-                row = rowval[j]
-                for i = lastrow+1:row-1
-                    R[i, col] = abs2(means[i, col])
-                end
-                R[row, col] = abs2(nzval[j] - means[row, col])
-                lastrow = row
-            end
-            for i = lastrow+1:m
-                R[i, col] = abs2(means[i, col])
-            end
-        end
-    end
-    return R
-end
-
 end # module