Add tests

test/Project.toml

@@ -17,6 +17,7 @@ LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsJuMP = "792afdf1-32c1-5681-94e0-d7bf7a5df49e"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 OptimizationProblems = "5049e819-d29b-5fba-b941-0eee7e64c1c6"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 PkgJogger = "10150987-6cc1-4b76-abee-b1c1cbd91c01"

test/classification.jl

@@ -30,6 +30,7 @@ using SparseConnectivityTracer: # testing
 using SpecialFunctions: SpecialFunctions
 using NNlib: NNlib
 using LogExpFunctions: LogExpFunctions
+using NaNMath: NaNMath
 using Test
 using ForwardDiff: derivative, gradient, hessian
 
@@ -50,6 +51,9 @@ random_input(::typeof(LogExpFunctions.log2mexp)) = -rand()  # log2mexp(x) is def
 random_input(::typeof(LogExpFunctions.logitexp)) = -rand()  # logitexp(x) is defined for x < 0
 random_input(::typeof(LogExpFunctions.logit1mexp)) = -rand() # logit1mexp(x) is defined for x < 0
 
+# NaNMath.jl
+random_input(::typeof(NaNMath.acosh)) = 1 + rand()  # Range: [1, ∞)
+
 random_first_input(op) = random_input(op)
 random_second_input(op) = random_input(op)
 
@@ -97,7 +101,7 @@ function correct_classification_1_to_1(op, x; atol)
 end
 
 @testset verbose = true "1-to-1" begin
-    @testset "$m" for m in (Base, SpecialFunctions, NNlib, LogExpFunctions)
+    @testset "$m" for m in (Base, SpecialFunctions, NNlib, LogExpFunctions, NaNMath)
         @testset "$op" for op in test_operators_1_to_1(Val(Symbol(m)))
             @test all(
                 correct_classification_1_to_1(op, random_input(op); atol=DEFAULT_ATOL) for
@@ -140,7 +144,7 @@ function correct_classification_2_to_1(op, x, y; atol)
 end
 
 @testset verbose = true "2-to-1" begin
-    @testset "$m" for m in (Base, SpecialFunctions, NNlib, LogExpFunctions)
+    @testset "$m" for m in (Base, SpecialFunctions, NNlib, LogExpFunctions, NaNMath)
         @testset "$op" for op in test_operators_2_to_1(Val(Symbol(m)))
             @test all(
                 correct_classification_2_to_1(
@@ -180,7 +184,7 @@ function correct_classification_1_to_2(op, x; atol)
 end
 
 @testset verbose = true "1-to-2" begin
-    @testset "$m" for m in (Base, SpecialFunctions, NNlib, LogExpFunctions)
+    @testset "$m" for m in (Base, SpecialFunctions, NNlib, LogExpFunctions, NaNMath)
         @testset "$op" for op in test_operators_1_to_2(Val(Symbol(m)))
             @test all(
                 correct_classification_1_to_2(op, random_input(op); atol=DEFAULT_ATOL) for

test/ext/test_NaNMath.jl

@@ -0,0 +1,78 @@
+using SparseConnectivityTracer
+using NaNMath
+using Test
+
+# Load definitions of GRADIENT_TRACERS, GRADIENT_PATTERNS, HESSIAN_TRACERS and HESSIAN_PATTERNS
+include("../tracers_definitions.jl")
+
+nan_1_to_1 = (
+    NaNMath.sqrt,
+    NaNMath.sin,
+    NaNMath.cos,
+    NaNMath.tan,
+    NaNMath.asin,
+    NaNMath.acos,
+    NaNMath.acosh,
+    NaNMath.atanh,
+    NaNMath.log,
+    NaNMath.log2,
+    NaNMath.log10,
+    NaNMath.log1p,
+    NaNMath.lgamma,
+)
+
+@testset "Jacobian Global" begin
+    method = TracerSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, method)
+
+    @testset "1-to-1 functions" begin
+        @testset "$f" for f in nan_1_to_1
+            @test J(x -> f(x[1]), rand(2)) == [1 0]
+        end
+    end
+    @testset "2-to-1 functions" begin
+        @test J(x -> NaNMath.pow(x[1], x[2]), rand(3)) == [1 1 0]
+        @test J(x -> NaNMath.max(x[1], x[2]), rand(3)) == [1 1 0]
+        @test J(x -> NaNMath.min(x[1], x[2]), rand(3)) == [1 1 0]
+    end
+end
+
+@testset "Jacobian Local" begin
+    method = TracerLocalSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, method)
+
+    @testset "2-to-1 functions" begin
+        @test J(x -> NaNMath.max(x[1], x[2]), [1.0, 2.0, 0.0]) == [0 1 0]
+        @test J(x -> NaNMath.max(x[1], x[2]), [2.0, 1.0, 0.0]) == [1 0 0]
+        @test J(x -> NaNMath.min(x[1], x[2]), [1.0, 2.0, 0.0]) == [1 0 0]
+        @test J(x -> NaNMath.min(x[1], x[2]), [2.0, 1.0, 0.0]) == [0 1 0]
+    end
+end
+
+@testset "Hessian Global" begin
+    method = TracerSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, method)
+
+    @testset "1-to-1 functions" begin
+        @testset "$f" for f in nan_1_to_1
+            @test H(x -> f(x[1]), rand(2)) == [1 0; 0 0]
+        end
+    end
+    @testset "2-to-1 functions" begin
+        @test H(x -> NaNMath.pow(x[1], x[2]), rand(3)) == [1 1 0; 1 1 0; 0 0 0]
+        @test H(x -> NaNMath.max(x[1], x[2]), rand(3)) == zeros(Bool, 3, 3)
+        @test H(x -> NaNMath.min(x[1], x[2]), rand(3)) == zeros(Bool, 3, 3)
+    end
+end
+
+@testset "Hessian Local" begin
+    method = TracerLocalSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, method)
+
+    @testset "2-to-1 functions" begin
+        @test H(x -> NaNMath.max(x[1], x[2]), [1.0, 2.0, 0.0]) == zeros(Bool, 3, 3)
+        @test H(x -> NaNMath.max(x[1], x[2]), [2.0, 1.0, 0.0]) == zeros(Bool, 3, 3)
+        @test H(x -> NaNMath.min(x[1], x[2]), [1.0, 2.0, 0.0]) == zeros(Bool, 3, 3)
+        @test H(x -> NaNMath.min(x[1], x[2]), [2.0, 1.0, 0.0]) == zeros(Bool, 3, 3)
+    end
+end

test/runtests.jl

@@ -94,7 +94,7 @@ GROUP = get(ENV, "JULIA_SCT_TEST_GROUP", "Core")
     if GROUP in ("Core", "All")
         @info "Testing package extensions..."
         @testset verbose = true "Package extensions" begin
-            for ext in (:NNlib, :SpecialFunctions, :LogExpFunctions)
+            for ext in (:NNlib, :SpecialFunctions, :LogExpFunctions, :NaNMath)
                 @testset "$ext" begin
                     @info "...$ext"
                     include("ext/test_$ext.jl")