Fix some tests due to an MTK update (and something related to FiniteD…

…iff.jl) (#311) * Fix the OOP test (which came with a MTK upgrade) * Fix an autodiff test (not only MTK but also FiniteDiff.jl changed) * JuliaFormatter.jl
nathanaelbosch · Apr 9, 2024 · 0525b2d · 0525b2d
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Showing 3 changed files with 20 additions and 7 deletions.
diff --git a/test/Project.toml b/test/Project.toml
@@ -4,6 +4,7 @@ BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
 DiffEqDevTools = "f3b72e0c-5b89-59e1-b016-84e28bfd966d"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
+FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 FiniteHorizonGramians = "b59a298d-d283-4a37-9369-85a9f9a111a5"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"

diff --git a/test/autodiff.jl b/test/autodiff.jl
@@ -2,7 +2,7 @@ using ProbNumDiffEq
 using ModelingToolkit
 using Test
 using LinearAlgebra
-using FiniteDiff
+using FiniteDifferences
 using ForwardDiff
 # using ReverseDiff
 # using Zygote
@@ -11,7 +11,14 @@ import ODEProblemLibrary: prob_ode_fitzhughnagumo
 
 @testset "solver: $ALG" for ALG in (EK0, EK1, DiagonalEK1)
     _prob = prob_ode_fitzhughnagumo
-    prob = ODEProblem(modelingtoolkitize(_prob), _prob.u0, _prob.tspan, jac=true)
+    prob = ODEProblem(
+        structural_simplify(modelingtoolkitize(_prob)),
+        _prob.u0,
+        _prob.tspan,
+        jac=true,
+    )
+    prob = remake(prob, p=collect(_prob.p))
+
     function param_to_loss(p)
         sol = solve(
             remake(prob, p=p),
@@ -37,12 +44,15 @@ import ODEProblemLibrary: prob_ode_fitzhughnagumo
         return norm(sol.u[end])  # Dummy loss
     end
 
-    dldp = FiniteDiff.finite_difference_gradient(param_to_loss, prob.p)
-    dldu0 = FiniteDiff.finite_difference_gradient(startval_to_loss, prob.u0)
+    # dldp = FiniteDiff.finite_difference_gradient(param_to_loss, prob.p)
+    # dldu0 = FiniteDiff.finite_difference_gradient(startval_to_loss, prob.u0)
+    # For some reason FiniteDiff.jl is not working anymore so we use FiniteDifferences.jl:
+    dldp = grad(central_fdm(5, 1), param_to_loss, prob.p)[1]
+    dldu0 = grad(central_fdm(5, 1), startval_to_loss, prob.u0)[1]
 
     @testset "ForwardDiff.jl" begin
-        @test ForwardDiff.gradient(param_to_loss, prob.p) ≈ dldp rtol = 1e-3
-        @test ForwardDiff.gradient(startval_to_loss, prob.u0) ≈ dldu0 rtol = 5e-3
+        @test ForwardDiff.gradient(param_to_loss, prob.p) ≈ dldp rtol = 1e-2
+        @test ForwardDiff.gradient(startval_to_loss, prob.u0) ≈ dldu0 rtol = 5e-2
     end
 
     # @testset "ReverseDiff.jl" begin

diff --git a/test/oop_problems.jl b/test/oop_problems.jl
@@ -14,7 +14,9 @@ using Test
     end
     @testset "with jacobian" begin
         # now with defined jac
-        prob = ODEProblem(modelingtoolkitize(prob), prob.u0, prob.tspan, jac=true)
+        prob = ODEProblem(
+            structural_simplify(modelingtoolkitize(prob)), prob.u0, prob.tspan, jac=true,
+        )
         @test solve(prob, EK0(order=4)) isa ProbNumDiffEq.ProbODESolution
         @test solve(prob, EK1(order=4)) isa ProbNumDiffEq.ProbODESolution
         @test solve(prob, EK1(order=4, initialization=ClassicSolverInit())) isa