Use SafeTestsets?

Project.toml

@@ -29,8 +29,9 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Kronecker = "2c470bb0-bcc8-11e8-3dad-c9649493f05e"
 PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Random", "Test", "FiniteDifferences", "Zygote", "PDMats", "Kronecker", "Flux"]
+test = ["SafeTestsets", "Random", "Test", "FiniteDifferences", "Zygote", "PDMats", "Kronecker", "Flux"]

test/approximations/nystrom.jl

@@ -1,9 +1,13 @@
-@testset "nystrom" begin
-    dims = [10,5]
-    X = rand(dims...)
-    k = SqExponentialKernel()
-    for obsdim in [1, 2]
-        @test kernelmatrix(k, X; obsdim=obsdim) ≈ kernelmatrix(nystrom(k, X, 1.0; obsdim=obsdim))
-        @test kernelmatrix(k, X; obsdim=obsdim) ≈ kernelmatrix(nystrom(k, X, collect(1:dims[obsdim]); obsdim=obsdim))
-    end
+using KernelFunctions
+
+using Test
+
+dims = [10,5]
+X = rand(dims...)
+k = SqExponentialKernel()
+for obsdim in [1, 2]
+    @test kernelmatrix(k, X; obsdim = obsdim) ≈
+        kernelmatrix(nystrom(k, X, 1.0; obsdim = obsdim))
+    @test kernelmatrix(k, X; obsdim = obsdim) ≈
+        kernelmatrix(nystrom(k, X, collect(1:dims[obsdim]); obsdim = obsdim))
 end

test/distances/delta.jl

@@ -1,10 +1,14 @@
-@testset "delta" begin
-    A = rand(10,5)
-    B = rand(20,5)
-    d = KernelFunctions.Delta()
-    @test pairwise(d,A,dims=1) == Matrix(I,size(A,1),size(A,1))
-    @test pairwise(d,A,B,dims=1) == zeros(size(A,1),size(B,1))
-    @test d(1,2) == 0
-    @test d(1,1) == 1
-    @test_throws DimensionMismatch d(rand(3),rand(4))
-end
+using KernelFunctions
+using Distances
+
+using LinearAlgebra
+using Test
+
+A = rand(10, 5)
+B = rand(20, 5)
+d = KernelFunctions.Delta()
+@test pairwise(d, A, dims = 1) == Matrix(I, size(A, 1), size(A, 1))
+@test pairwise(d, A, B, dims = 1) == zeros(size(A, 1), size(B, 1))
+@test d(1, 2) == 0
+@test d(1, 1) == 1
+@test_throws DimensionMismatch d(rand(3), rand(4))

test/distances/dotproduct.jl

@@ -1,8 +1,12 @@
-@testset "dotproduct" begin
-    A = rand(10,5)
-    B = rand(20,5)
-    d = KernelFunctions.DotProduct()
-    @test diag(pairwise(d,A,dims=2)) == [dot(A[:,i],A[:,i]) for i in 1:size(A,2)]
-    @test_throws DimensionMismatch d(rand(3),rand(4))
-    @test d(3.0,2.0) == 6.0
-end
+using KernelFunctions
+using Distances
+
+using LinearAlgebra
+using Test
+
+A = rand(10, 5)
+B = rand(20, 5)
+d = KernelFunctions.DotProduct()
+@test diag(pairwise(d, A, dims = 2)) == [dot(A[:,i], A[:,i]) for i in 1:size(A, 2)]
+@test_throws DimensionMismatch d(rand(3), rand(4))
+@test d(3.0, 2.0) == 6.0

test/generic.jl

@@ -1,6 +1,8 @@
-@testset "generic" begin
-    k = SqExponentialKernel()
-    @test length(k) == 1
-    @test iterate(k) == (k,nothing)
-    @test iterate(k,1) == nothing
-end
+using KernelFunctions
+
+using Test
+
+k = SqExponentialKernel()
+@test length(k) == 1
+@test iterate(k) == (k, nothing)
+@test iterate(k, 1) == nothing

test/kernels/constant.jl

@@ -1,25 +1,28 @@
-@testset "constant" begin
-    @testset "ZeroKernel" begin
-        k = ZeroKernel()
-        @test eltype(k) == Any
-        @test kappa(k,2.0) == 0.0
-        @test KernelFunctions.metric(ZeroKernel()) == KernelFunctions.Delta()
-    end
-    @testset "WhiteKernel" begin
-        k = WhiteKernel()
-        @test eltype(k) == Any
-        @test kappa(k,1.0) == 1.0
-        @test kappa(k,0.0) == 0.0
-        @test EyeKernel == WhiteKernel
-        @test metric(WhiteKernel()) == KernelFunctions.Delta()
-    end
-    @testset "ConstantKernel" begin
-        c = 2.0
-        k = ConstantKernel(c=c)
-        @test eltype(k) == Any
-        @test kappa(k,1.0) == c
-        @test kappa(k,0.5) == c
-        @test metric(ConstantKernel()) == KernelFunctions.Delta()
-        @test metric(ConstantKernel(c=2.0)) == KernelFunctions.Delta()
-    end
+using KernelFunctions
+using KernelFunctions: metric
+
+using Test
+
+@testset "ZeroKernel" begin
+    k = ZeroKernel()
+    @test eltype(k) == Any
+    @test kappa(k, 2.0) == 0.0
+    @test KernelFunctions.metric(ZeroKernel()) == KernelFunctions.Delta()
+end
+@testset "WhiteKernel" begin
+    k = WhiteKernel()
+    @test eltype(k) == Any
+    @test kappa(k, 1.0) == 1.0
+    @test kappa(k, 0.0) == 0.0
+    @test EyeKernel == WhiteKernel
+    @test metric(WhiteKernel()) == KernelFunctions.Delta()
+end
+@testset "ConstantKernel" begin
+    c = 2.0
+    k = ConstantKernel(c = c)
+    @test eltype(k) == Any
+    @test kappa(k, 1.0) == c
+    @test kappa(k, 0.5) == c
+    @test metric(ConstantKernel()) == KernelFunctions.Delta()
+    @test metric(ConstantKernel(c = 2.0)) == KernelFunctions.Delta()
 end

test/kernels/cosine.jl

@@ -1,14 +1,17 @@
-@testset "cosine" begin
-    rng = MersenneTwister(123456)
-    x = rand(rng)*2
-    v1 = rand(rng, 3)
-    v2 = rand(rng, 3)
+using KernelFunctions
 
-    k = CosineKernel()
-    @test eltype(k) == Any
-    @test kappa(k, 1.0) ≈ -1.0 atol=1e-5
-    @test kappa(k, 2.0) ≈ 1.0 atol=1e-5
-    @test kappa(k, 1.5) ≈ 0.0 atol=1e-5
-    @test kappa(k,x) ≈ cospi(x) atol=1e-5
-    @test k(v1, v2) ≈ cospi(sqrt(sum(abs2.(v1-v2)))) atol=1e-5
-end
+using Random
+using Test
+
+rng = MersenneTwister(123456)
+x = rand(rng) * 2
+v1 = rand(rng, 3)
+v2 = rand(rng, 3)
+
+k = CosineKernel()
+@test eltype(k) == Any
+@test kappa(k, 1.0) ≈ -1.0 atol = 1e-5
+@test kappa(k, 2.0) ≈ 1.0 atol = 1e-5
+@test kappa(k, 1.5) ≈ 0.0 atol = 1e-5
+@test kappa(k, x) ≈ cospi(x) atol = 1e-5
+@test k(v1, v2) ≈ cospi(sqrt(sum(abs2.(v1 - v2)))) atol = 1e-5

test/kernels/custom.jl

@@ -1,3 +1,8 @@
+using KernelFunctions
+using Distances
+
+using Test
+
 # minimal definition of a custom kernel
 struct MyKernel <: Kernel end
 
@@ -9,14 +14,12 @@ KernelFunctions.metric(::MyKernel) = SqEuclidean()
 (κ::MyKernel)(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real}; obsdim = 2) = kernelmatrix(κ, X, Y; obsdim = obsdim)
 (κ::MyKernel)(X::AbstractMatrix{<:Real}; obsdim = 2) = kernelmatrix(κ, X; obsdim = obsdim)
 
-@testset "custom" begin
-    @test kappa(MyKernel(), 3) == kappa(SqExponentialKernel(), 3)
-    @test kappa(MyKernel(), 1, 3) == kappa(SqExponentialKernel(), 1, 3)
-    @test kappa(MyKernel(), [1, 2], [3, 4]) == kappa(SqExponentialKernel(), [1, 2], [3, 4])
-    @test kernelmatrix(MyKernel(), [1 2; 3 4], [5 6; 7 8]) == kernelmatrix(SqExponentialKernel(), [1 2; 3 4], [5 6; 7 8])
-    @test kernelmatrix(MyKernel(), [1 2; 3 4]) == kernelmatrix(SqExponentialKernel(), [1 2; 3 4])
+@test kappa(MyKernel(), 3) == kappa(SqExponentialKernel(), 3)
+@test kappa(MyKernel(), 1, 3) == kappa(SqExponentialKernel(), 1, 3)
+@test kappa(MyKernel(), [1, 2], [3, 4]) == kappa(SqExponentialKernel(), [1, 2], [3, 4])
+@test kernelmatrix(MyKernel(), [1 2; 3 4], [5 6; 7 8]) == kernelmatrix(SqExponentialKernel(), [1 2; 3 4], [5 6; 7 8])
+@test kernelmatrix(MyKernel(), [1 2; 3 4]) == kernelmatrix(SqExponentialKernel(), [1 2; 3 4])
 
-    @test MyKernel()([1, 2], [3, 4]) == SqExponentialKernel()([1, 2], [3, 4])
-    @test MyKernel()([1 2; 3 4], [5 6; 7 8]) == SqExponentialKernel()([1 2; 3 4], [5 6; 7 8])
-    @test MyKernel()([1 2; 3 4]) == SqExponentialKernel()([1 2; 3 4])
-end
+@test MyKernel()([1, 2], [3, 4]) == SqExponentialKernel()([1, 2], [3, 4])
+@test MyKernel()([1 2; 3 4], [5 6; 7 8]) == SqExponentialKernel()([1 2; 3 4], [5 6; 7 8])
+@test MyKernel()([1 2; 3 4]) == SqExponentialKernel()([1 2; 3 4])

test/kernels/exponential.jl

@@ -1,34 +1,41 @@
-@testset "exponential" begin
-    rng = MersenneTwister(123456)
-    x = rand(rng)*2
-    v1 = rand(rng, 3)
-    v2 = rand(rng, 3)
-    @testset "SqExponentialKernel" begin
-        k = SqExponentialKernel()
-        @test kappa(k,x) ≈ exp(-x)
-        @test k(v1,v2) ≈ exp(-norm(v1-v2)^2)
-        @test kappa(SqExponentialKernel(),x) == kappa(k,x)
-        @test metric(SqExponentialKernel()) == SqEuclidean()
-    end
-    @testset "ExponentialKernel" begin
-        k = ExponentialKernel()
-        @test kappa(k,x) ≈ exp(-x)
-        @test k(v1,v2) ≈ exp(-norm(v1-v2))
-        @test kappa(ExponentialKernel(),x) == kappa(k,x)
-        @test metric(ExponentialKernel()) == Euclidean()
-    end
-    @testset "GammaExponentialKernel" begin
-        γ = 2.0
-        k = GammaExponentialKernel(γ=γ)
-        @test kappa(k,x) ≈ exp(-(x)^(γ))
-        @test k(v1,v2) ≈ exp(-norm(v1-v2)^(2γ))
-        @test kappa(GammaExponentialKernel(),x) == kappa(k,x)
-        @test GammaExponentialKernel(gamma=γ).γ == [γ]
-        @test metric(GammaExponentialKernel()) == SqEuclidean()
-        @test metric(GammaExponentialKernel(γ=2.0)) == SqEuclidean()
+using KernelFunctions
+using KernelFunctions: metric
+using Distances
 
-        #Coherence :
-        @test KernelFunctions._kernel(GammaExponentialKernel(γ=1.0),v1,v2) ≈ KernelFunctions._kernel(SqExponentialKernel(),v1,v2)
-        @test KernelFunctions._kernel(GammaExponentialKernel(γ=0.5),v1,v2) ≈ KernelFunctions._kernel(ExponentialKernel(),v1,v2)
-    end
+using LinearAlgebra
+using Random
+using Test
+
+rng = MersenneTwister(123456)
+x = rand(rng) * 2
+v1 = rand(rng, 3)
+v2 = rand(rng, 3)
+@testset "SqExponentialKernel" begin
+    k = SqExponentialKernel()
+    @test kappa(k, x) ≈ exp(-x)
+    @test k(v1, v2) ≈ exp(-norm(v1 - v2)^2)
+    @test kappa(SqExponentialKernel(), x) == kappa(k, x)
+    @test metric(SqExponentialKernel()) == SqEuclidean()
+end
+@testset "ExponentialKernel" begin
+    k = ExponentialKernel()
+    @test kappa(k, x) ≈ exp(-x)
+    @test k(v1, v2) ≈ exp(-norm(v1 - v2))
+    @test kappa(ExponentialKernel(), x) == kappa(k, x)
+    @test metric(ExponentialKernel()) == Euclidean()
 end
+@testset "GammaExponentialKernel" begin
+    γ = 2.0
+    k = GammaExponentialKernel(γ = γ)
+    @test kappa(k, x) ≈ exp(-(x)^(γ))
+    @test k(v1, v2) ≈ exp(-norm(v1 - v2)^(2γ))
+    @test kappa(GammaExponentialKernel(), x) == kappa(k, x)
+    @test GammaExponentialKernel(gamma = γ).γ == [γ]
+    @test metric(GammaExponentialKernel()) == SqEuclidean()
+    @test metric(GammaExponentialKernel(γ = 2.0)) == SqEuclidean()
+
+        # Coherence :
+    @test KernelFunctions._kernel(GammaExponentialKernel(γ = 1.0), v1, v2) ≈ KernelFunctions._kernel(SqExponentialKernel(), v1, v2)
+    @test KernelFunctions._kernel(GammaExponentialKernel(γ = 0.5), v1, v2) ≈ KernelFunctions._kernel(ExponentialKernel(), v1, v2)
+end
+

test/kernels/exponentiated.jl

@@ -1,12 +1,17 @@
-@testset "exponentiated" begin
-    rng = MersenneTwister(123456)
-    x = rand(rng)*2
-    v1 = rand(rng, 3)
-    v2 = rand(rng, 3)
+using KernelFunctions
+using KernelFunctions: metric
 
-    k = ExponentiatedKernel()
-    @test kappa(k,x) ≈ exp(x)
-    @test kappa(k,-x) ≈ exp(-x)
-    @test k(v1,v2) ≈ exp(dot(v1,v2))
-    @test metric(ExponentiatedKernel()) == KernelFunctions.DotProduct()
-end
+using LinearAlgebra
+using Random
+using Test
+
+rng = MersenneTwister(123456)
+x = rand(rng) * 2
+v1 = rand(rng, 3)
+v2 = rand(rng, 3)
+
+k = ExponentiatedKernel()
+@test kappa(k, x) ≈ exp(x)
+@test kappa(k, -x) ≈ exp(-x)
+@test k(v1, v2) ≈ exp(dot(v1, v2))
+@test metric(ExponentiatedKernel()) == KernelFunctions.DotProduct()

test/kernels/fbm.jl

@@ -1,17 +1,20 @@
-@testset "FBM" begin
-    h = 0.3
-    k = FBMKernel(h = h)
-    v1 = rand(3); v2 = rand(3)
-    @test k(v1,v2) ≈ (sqeuclidean(v1, zero(v1))^h + sqeuclidean(v2, zero(v2))^h - sqeuclidean(v1-v2, zero(v1-v2))^h)/2 atol=1e-5
+using KernelFunctions
+using Distances
+
+using Test
+
+h = 0.3
+k = FBMKernel(h = h)
+v1 = rand(3); v2 = rand(3)
+@test k(v1, v2) ≈ (sqeuclidean(v1, zero(v1))^h + sqeuclidean(v2, zero(v2))^h - sqeuclidean(v1 - v2, zero(v1 - v2))^h) / 2 atol = 1e-5
 
     # kernelmatrix tests
-    m1 = rand(3,3)
-    m2 = rand(3,3)
-    @test kernelmatrix(k, m1, m1) ≈ kernelmatrix(k, m1) atol=1e-5
-    @test kernelmatrix(k, m1, m2) ≈ k(m1, m2) atol=1e-5
+m1 = rand(3, 3)
+m2 = rand(3, 3)
+@test kernelmatrix(k, m1, m1) ≈ kernelmatrix(k, m1) atol = 1e-5
+@test kernelmatrix(k, m1, m2) ≈ k(m1, m2) atol = 1e-5
 
 
-    x1 = rand()
-    x2 = rand()
-    @test kernelmatrix(k, x1*ones(1,1), x2*ones(1,1))[1] ≈ k(x1, x2) atol=1e-5
-end
+x1 = rand()
+x2 = rand()
+@test kernelmatrix(k, x1 * ones(1, 1), x2 * ones(1, 1))[1] ≈ k(x1, x2) atol = 1e-5

test/kernels/kernelproduct.jl

@@ -1,15 +1,18 @@
-@testset "kernelproduct" begin
-    rng = MersenneTwister(123456)
-    v1 = rand(rng, 3)
-    v2 = rand(rng, 3)
-
-    k1 = LinearKernel()
-    k2 = SqExponentialKernel()
-    k3 = RationalQuadraticKernel()
-
-    k = KernelProduct([k1,k2])
-    @test length(k) == 2
-    @test kappa(k,v1,v2) == kappa(k1*k2,v1,v2)
-    @test kappa(k*k,v1,v2) ≈ kappa(k,v1,v2)^2
-    @test kappa(k*k3,v1,v2) ≈ kappa(k3*k,v1,v2)
-end
+using KernelFunctions
+
+using Random
+using Test
+
+rng = MersenneTwister(123456)
+v1 = rand(rng, 3)
+v2 = rand(rng, 3)
+
+k1 = LinearKernel()
+k2 = SqExponentialKernel()
+k3 = RationalQuadraticKernel()
+
+k = KernelProduct([k1,k2])
+@test length(k) == 2
+@test kappa(k, v1, v2) == kappa(k1 * k2, v1, v2)
+@test kappa(k * k, v1, v2) ≈ kappa(k, v1, v2)^2
+@test kappa(k * k3, v1, v2) ≈ kappa(k3 * k, v1, v2)