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Move rewrite tests to src/Test
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@blegat
blegat committed Dec 1, 2019
1 parent 4314b92 commit 52ea63f
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9 changes: 4 additions & 5 deletions src/MutableArithmetics.jl
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Expand Up @@ -26,22 +26,21 @@ include("interface.jl")
include("shortcuts.jl")
include("broadcast.jl")

# Test that can be used to test an implementation of the interface
include("Test/Test.jl")

# Implementation of the interface for Base types
import LinearAlgebra
const Scaling = Union{Number, LinearAlgebra.UniformScaling}
scaling(x::Scaling) = x
include("bigint.jl")
include("linear_algebra.jl")
include("sparse_arrays.jl")

isequal_canonical(a, b) = a == b

include("rewrite.jl")
include("dispatch.jl")

include("sparse_arrays.jl")
# Test that can be used to test an implementation of the interface
include("Test/Test.jl")

include("dispatch.jl")

end # module
7 changes: 6 additions & 1 deletion src/Test/Test.jl
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Expand Up @@ -3,10 +3,15 @@ module Test
import MutableArithmetics
const MA = MutableArithmetics

using Test
using LinearAlgebra, SparseArrays, Test

include("config.jl")

include("int.jl")
include("generic.jl")
include("scalar.jl")
include("quadratic.jl")
include("array.jl")
include("sparse.jl")

end # module
352 changes: 352 additions & 0 deletions src/Test/array.jl
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@@ -0,0 +1,352 @@
function matrix_vector_division_test(x)
if size(x) == (3, 3)
A = [2 1 0
1 2 1
0 1 2]
@test_rewrite A * x / 2
end
end

function _xAx_test(x::AbstractVector, A::AbstractMatrix)
@test_rewrite(x' * A)
# Complex expression
@test_rewrite(x' * ones(Int, size(A)...))
@test_rewrite(x' * A * x)
# Complex expression
@test_rewrite(x' * ones(Int, size(A)...) * x)
@test_rewrite reshape(x, (1, length(x))) * A * x .- 1
@test_rewrite x' * A * x .- 1
@test_rewrite x' * A * x - 1
end
function _xABx_test(x::AbstractVector, A::AbstractMatrix, B::AbstractMatrix)
@test_rewrite (x'A)' + 2B * x
@test_rewrite (x'A)' + 2B * x .- 1
@test_rewrite (x'A)' + 2B * x .- [length(x):-1:1;]
@test_rewrite (x'A)' + 2B * x - [length(x):-1:1;]
end

function _matrix_vector_test(x::AbstractVector, A::AbstractMatrix)
_xAx_test(x, A)
B = sparse(A)
_xAx_test(x, B)

@test MA.isequal_canonical(A * x, B * x)
@test MA.isequal_canonical(A * x, MA.@rewrite(B * x))
@test MA.isequal_canonical(MA.@rewrite(A * x), MA.@rewrite(B * x))
@test MA.isequal_canonical(x' * A, x' * B)
@test MA.isequal_canonical(x' * A, MA.@rewrite(x' * B))
@test MA.isequal_canonical(MA.@rewrite(x' * A), MA.@rewrite(x' * B))
@test MA.isequal_canonical(x'A*x, x'*B*x)
@test MA.isequal_canonical(x'*A*x, MA.@rewrite(x'*B*x))
@test MA.isequal_canonical(MA.@rewrite(x'*A*x), MA.@rewrite(x'*B*x))

_xABx_test(x, A, A)
_xABx_test(x, A, B)
_xABx_test(x, B, A)
_xABx_test(x, B, B)
end

function matrix_vector_test(x)
add_test(x, x)
if size(x) != (3,)
return
end
A = [2 1 0
1 2 1
0 1 2]
@test MA.isequal_canonical(-x, [-x[1], -x[2], -x[3]])
xAx = 2x[1]*x[1] + 2x[1]*x[2] + 2x[2]*x[2] + 2x[2]*x[3] + 2x[3]*x[3]
@test MA.isequal_canonical(x' * A * x, xAx)
y = A * x
@test MA.isequal_canonical(x' * y, xAx)
@test MA.isequal_canonical(y, [
2x[1] + x[2]
2x[2] + x[1] + x[3]
x[2] + 2x[3]])
@test MA.isequal_canonical(-y, [
-2x[1] - x[2]
-x[1] - 2x[2] - x[3]
-x[2] - 2x[3]])
@test MA.isequal_canonical(x' * A, [
2x[1] + x[2];
x[1] + 2x[2] + x[3];
x[2] + 2x[3]]')

@test MA.isequal_canonical(y .+ 1, [
2x[1] + x[2] + 1
x[1] + 2x[2] + x[3] + 1
x[2] + 2x[3] + 1])
@test MA.isequal_canonical(y .- 1, [
2x[1] + x[2] - 1
x[1] + 2x[2] + x[3] - 1
x[2] + 2x[3] - 1])
@test MA.isequal_canonical(y .+ 2ones(Int, 3), [
2x[1] + x[2] + 2
x[1] + 2x[2] + x[3] + 2
x[2] + 2x[3] + 2])
@test MA.isequal_canonical(y .- 2ones(Int, 3), [
2x[1] + x[2] - 2
x[1] + 2x[2] + x[3] - 2
x[2] + 2x[3] - 2])
@test MA.isequal_canonical(2ones(Int, 3) .+ y, [
2x[1] + x[2] + 2
x[1] + 2x[2] + x[3] + 2
x[2] + 2x[3] + 2])
@test MA.isequal_canonical(2ones(Int, 3) .- y, [
-2x[1] - x[2] + 2
-x[1] - 2x[2] - x[3] + 2
-x[2] - 2x[3] + 2])
@test MA.isequal_canonical(y .+ x, [
3x[1] + x[2]
x[1] + 3x[2] + x[3]
x[2] + 3x[3]])
@test MA.isequal_canonical(x .+ y, [
3x[1] + x[2]
x[1] + 3x[2] + x[3]
x[2] + 3x[3]])
@test MA.isequal_canonical(2y .+ 2x, [
6x[1] + 2x[2]
2x[1] + 6x[2] + 2x[3]
2x[2] + 6x[3]])
@test MA.isequal_canonical(y .- x, [
x[1] + x[2]
x[1] + x[2] + x[3]
x[2] + x[3]])
@test MA.isequal_canonical(x .- y, [
-x[1] - x[2]
-x[1] - x[2] - x[3]
-x[2] - x[3]])
@test MA.isequal_canonical(y .+ x[:], [
3x[1] + x[2]
x[1] + 3x[2] + x[3]
x[2] + 3x[3]])
@test MA.isequal_canonical(x[:] .+ y, [
3x[1] + x[2]
x[1] + 3x[2] + x[3]
x[2] + 3x[3]])

_matrix_vector_test(x, A)

A = [1 2 3
0 4 5
6 0 7]
_matrix_vector_test(x, A)
end

_constant(x) = reshape(collect(1:length(x)), size(x)...)

function non_array_test(x, x2)
# This is needed to compare arrays that have nonstandard indexing
elements_equal(A::AbstractArray{T, N}, B::AbstractArray{T, N}) where {T, N} = all(a == b for (a, b) in zip(A, B))

@test elements_equal(+x, +x2)
@test elements_equal(-x, -x2)
@test elements_equal(x .+ first(x), x2 .+ first(x2))
@test elements_equal(x .- first(x), x2 .- first(x2))
@test elements_equal(first(x) .- x, first(x2) .- x2)
@test elements_equal(first(x) .+ x, first(x2) .+ x2)
@test elements_equal(2 .* x, 2 .* x2)
@test elements_equal(first(x) .+ x2, first(x2) .+ x)
@test sum(x) == sum(x2)
if !MA._one_indexed(x2)
@test_throws DimensionMismatch x + x2
end
if x isa AbstractVector
# `diagm` only define with `Pair` in Julia v1.0 and v1.1
@testset "diagm" begin
if !MA._one_indexed(x2) && eltype(x2) <: MA.AbstractMutable
@test_throws AssertionError diagm(x2)
else
@test diagm(0 => x) == diagm(0 => x2)
if VERSION >= v"1.2"
@test diagm(x) == diagm(x2)
end
end
end
end
end

function non_array_test(x)
non_array_test(x, x)
if x isa AbstractVector
non_array_test(x, view(x, :))
end
if x isa AbstractVector || x isa AbstractMatrix
non_array_test(x, sparse(x))
end
end

function dot_test(x)
@test_rewrite dot(x[1], x[1])
@test_rewrite dot(2, x[1])
@test_rewrite dot(x[1], 2)

A = _constant(x)
@test_rewrite dot(A, x)
@test_rewrite dot(x, A)

y = repeat(x, outer = (one.(size(x))..., size(x, 1)))
@test_rewrite dot(x, _constant(x)) - dot(y, _constant(y))
end

function sum_test(matrix)
@test_rewrite sum(matrix)
if matrix isa AbstractMatrix
@test_rewrite sum([2matrix[i, j] for i in 1:size(matrix, 1), j in 1:size(matrix, 2)])
@test_rewrite sum(2matrix[i, j] for i in 1:size(matrix, 1), j in 1:size(matrix, 2))
@test_rewrite sum([2matrix[i, j]^2 for i in 1:size(matrix, 1), j in 1:size(matrix, 2)])
@test_rewrite sum(2matrix[i, j]^2 for i in 1:size(matrix, 1), j in 1:size(matrix, 2))
end
end

function transpose_test(x)
if x isa Vector
y = reshape(x, 1, length(x))
@test MA.isequal_canonical(x', y)
@test MA.isequal_canonical(copy(transpose(x)), y)
end
if x isa AbstractMatrix
y = [x[i, j] for j in 1:size(x, 2), i in 1:size(x, 1)]
@test MA.isequal_canonical(x', y)
@test MA.isequal_canonical(copy(transpose(x)), y)
end
if x isa AbstractVector || x isa AbstractMatrix
@test_rewrite(x')
@test_rewrite(transpose(x))
@test (x')' == x
@test transpose(transpose(x)) == x
end
end

function _broadcast_test(x, A)
B = sparse(A)
y = SparseMatrixCSC(size(x)..., copy(B.colptr), copy(B.rowval), vec(x))

@test MA.isequal_canonical(A .+ x, B .+ x)
@test MA.isequal_canonical(A .+ x, A .+ y)
@test MA.isequal_canonical(A .+ y, B .+ y)
@test MA.isequal_canonical(x .+ A, x .+ B)
@test MA.isequal_canonical(x .+ A, y .+ A)
@test MA.isequal_canonical(y .+ A, y .+ B)

@test MA.isequal_canonical(A .- x, B .- x)
@test MA.isequal_canonical(A .- x, A .- y)
@test MA.isequal_canonical(A .- y, B .- y)
@test MA.isequal_canonical(x .- A, x .- B)
@test MA.isequal_canonical(x .- A, y .- A)
@test MA.isequal_canonical(y .- A, y .- B)

@test MA.isequal_canonical(A .* x, B .* x)
@test MA.isequal_canonical(A .* x, A .* y)
@test MA.isequal_canonical(A .* y, B .* y)
@test MA.isequal_canonical(x .* A, x .* B)
@test MA.isequal_canonical(x .* A, y .* A)
@test MA.isequal_canonical(y .* A, y .* B)
end
function broadcast_test(x)
if !(x isa AbstractMatrix)
return
end
A = reshape(1:length(x), size(x)...)
if size(x) == (2, 2)
@test MA.isequal_canonical(A .+ x, [
1+x[1,1] 3+x[1,2];
2+x[2,1] 4+x[2,2]])
@test MA.isequal_canonical(x .+ A, [
1+x[1,1] 3+x[1,2];
2+x[2,1] 4+x[2,2]])
@test MA.isequal_canonical(x .+ x, [2x[1,1] 2x[1,2]; 2x[2,1] 2x[2,2]])

@test MA.isequal_canonical(A .- x, [
1-x[1,1] 3-x[1,2];
2-x[2,1] 4-x[2,2]])
@test MA.isequal_canonical(x .- x, [zero(x[1] - x[1]) for _1 in 1:2, _2 in 1:2])
@test MA.isequal_canonical(x .- A, [
-1+x[1,1] -3+x[1,2];
-2+x[2,1] -4+x[2,2]])

@test MA.isequal_canonical(A .* x, [
1*x[1,1] 3*x[1,2];
2*x[2,1] 4*x[2,2]])
@test MA.isequal_canonical(x .* A, [
1*x[1,1] 3*x[1,2];
2*x[2,1] 4*x[2,2]])
@test MA.isequal_canonical(x .* x, [x[1,1]^2 x[1,2]^2; x[2,1]^2 x[2,2]^2])

# TODO: Refactor to avoid calling the internal JuMP function
# `_densify_with_jump_eltype`.
#z = JuMP._densify_with_jump_eltype((2 .* y) ./ 3)
#@test MA.isequal_canonical((2 .* x) ./ 3, z)
#z = JuMP._densify_with_jump_eltype(2 * (y ./ 3))
#@test MA.isequal_canonical(2 .* (x ./ 3), z)
#z = JuMP._densify_with_jump_eltype((x[1,1],) .* B)
#@test MA.isequal_canonical((x[1,1],) .* A, z)
end
_broadcast_test(x, A)
end

function _broadcast_division_test(x, A)
B = sparse(A)
y = SparseMatrixCSC(size(x)..., copy(B.colptr), copy(B.rowval), vec(x))

@test MA.isequal_canonical(x ./ A, x ./ B)
@test MA.isequal_canonical(x ./ A, y ./ A)
end
function broadcast_division_test(x)
if !(x isa AbstractMatrix)
return
end
A = reshape(1:length(x), size(x)...)
if size(x) == (2, 2)
@test MA.isequal_canonical(x ./ A, [
x[1,1] / 1 x[1,2] / 3;
x[2,1] / 2 x[2,2] / 4])
end
_broadcast_division_test(x, A)
end

function symmetric_unary_test(x)
if x isa AbstractMatrix
unary_test(LinearAlgebra.Symmetric(x))
end
end

function matrix_uniform_scaling_test(x)
if !(x isa AbstractMatrix)
return
end
add_test(x, I)
@test_rewrite (x .+ 1) + I
@test_rewrite (x .- 1) - I
@test_rewrite I + (x .+ 1)
@test_rewrite I - (x .- 1)
@test_rewrite I * x
@test_rewrite I * (x .+ 1)
@test_rewrite (x .+ 1) * I
@test_rewrite (x .+ 1) + I * I
@test_rewrite (x .+ 1) + 2 * I
@test_rewrite (x .+ 1) + I * 2
end

function symmetric_matrix_uniform_scaling_test(x)
if x isa AbstractMatrix
matrix_uniform_scaling_test(LinearAlgebra.Symmetric(x))
end
end

const matrix_tests = Dict(
"matrix_vector_division" => matrix_vector_division_test,
"non_array" => non_array_test,
"matrix_vector" => matrix_vector_test,
"dot" => dot_test,
"sum" => sum_test,
"transpose" => transpose_test,
"broadcast" => broadcast_test,
"broadcast_division" => broadcast_division_test,
"unary" => unary_test,
"symmetric_unary" => symmetric_unary_test,
"matrix_uniform_scaling" => matrix_uniform_scaling_test,
"symmetric_matrix_uniform_scaling" => symmetric_matrix_uniform_scaling_test
)

@test_suite matrix

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