Fix BlackBoxOptim test calls to use Optimization.jl problem interface

Project.toml

@@ -41,14 +41,14 @@ BlackBoxOptim = "a134a8b2-14d6-55f6-9291-3336d3ab0209"
 DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
 ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationBBO = "3e6eede4-6085-4f62-9a71-46d9bc1eb92b"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-ParameterizedFunctions = "65888b18-ceab-5e60-b2b9-181511a3b968"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
@@ -58,4 +58,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Test", "ExplicitImports", "BlackBoxOptim", "DelayDiffEq", "ForwardDiff", "NLopt", "Optim", "Optimization", "OptimizationBBO", "OptimizationNLopt", "OptimizationOptimJL", "OrdinaryDiffEq", "ParameterizedFunctions", "Random", "SciMLSensitivity", "StochasticDiffEq", "SteadyStateDiffEq", "Sundials", "Zygote"]
+test = ["Test", "ExplicitImports", "BlackBoxOptim", "DelayDiffEq", "ForwardDiff", "Logging", "NLopt", "Optim", "Optimization", "OptimizationBBO", "OptimizationNLopt", "OptimizationOptimJL", "OrdinaryDiffEq", "Random", "SciMLSensitivity", "StochasticDiffEq", "SteadyStateDiffEq", "Sundials", "Zygote"]

src/cost_functions.jl

@@ -23,13 +23,14 @@ function prior_loss(prior, p)
     return ll
 end
 
-struct L2Loss{T, D, U, W, G} <: DiffEqBase.DECostFunction
+struct L2Loss{T, D, U, W, G, B} <: DiffEqBase.DECostFunction
     t::T
     data::D
     differ_weight::U
     data_weight::W
     colloc_grad::G
     dudt::G
+    du_buf::B
 end
 
 function (f::L2Loss)(sol::DiffEqBase.AbstractNoTimeSolution)
@@ -144,8 +145,10 @@ function (f::L2Loss)(sol::SciMLBase.AbstractSciMLSolution)
         end
     end
     if colloc_grad !== nothing
+        du_buf = f.du_buf
         for i in 1:size(colloc_grad)[2]
-            sol.prob.f.f(@view(dudt[:, i]), sol.u[i], sol.prob.p, sol.t[i])
+            sol.prob.f.f(du_buf, sol.u[i], sol.prob.p, sol.t[i])
+            dudt[:, i] .= du_buf
         end
         sumsq += sum(abs2, x - y for (x, y) in zip(dudt, colloc_grad))
     end
@@ -164,7 +167,8 @@ function L2Loss(
     return L2Loss(
         t, matrixize(data), matrixize(differ_weight),
         matrixize(data_weight), matrixize(colloc_grad),
-        colloc_grad === nothing ? nothing : zeros(size(colloc_grad))
+        colloc_grad === nothing ? nothing : zeros(size(colloc_grad)),
+        colloc_grad === nothing ? nothing : zeros(size(colloc_grad, 1))
     )
 end
 

test/likelihood.jl

@@ -69,9 +69,12 @@ optprob = Optimization.OptimizationProblem(
 )
 result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 11.0e3)
 @test result.u ≈ [1.5, 1.0] atol = 1.0e-1
-using OptimizationBBO.BlackBoxOptim
-result = bboptimize(obj, search_range = [(0.5, 5.0), (0.5, 5.0)], max_steps = 11.0e3)
-@test result.archive_output.best_candidate ≈ [1.5, 1.0] atol = 1.0e-1
+optprob = Optimization.OptimizationProblem(
+    obj, [2.0, 2.0],
+    lb = [0.5, 0.5], ub = [5.0, 5.0]
+)
+result = solve(optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 11.0e3)
+@test result.u ≈ [1.5, 1.0] atol = 1.0e-1
 
 distributions = [fit_mle(MvNormal, aggregate_data[:, j, :]) for j in 1:200]
 diff_distributions = [

test/multiple_shooting_objective_test.jl

@@ -1,4 +1,5 @@
-using OrdinaryDiffEq, DiffEqParamEstim, Distributions, Zygote
+using OrdinaryDiffEq, DiffEqParamEstim, Distributions, Zygote,
+    Optimization, OptimizationBBO, OptimizationOptimJL, Logging
 ms_f = function (du, u, p, t)
     du[1] = p[1] * u[1] - p[2] * u[1] * u[2]
     return du[2] = -3.0 * u[2] + u[1] * u[2]
@@ -30,8 +31,17 @@ ms_obj = multiple_shooting_objective(
     discontinuity_weight = 1.0, abstol = 1.0e-12,
     reltol = 1.0e-12
 )
-result = bboptimize(ms_obj; search_range = bound, max_steps = 21.0e3)
-@test result.archive_output.best_candidate[(end - 1):end] ≈ [1.5, 1.0] atol = 2.0e-1
+optprob = Optimization.OptimizationProblem(
+    ms_obj, fill(5.0, 18),
+    lb = first.(bound), ub = last.(bound)
+)
+result = with_logger(NullLogger()) do
+    solve(
+        optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(),
+        maxiters = 21000
+    )
+end
+@test result.u[(end - 1):end] ≈ [1.5, 1.0] atol = 2.0e-1
 
 priors = [Truncated(Normal(1.5, 0.5), 0, 2), Truncated(Normal(1.0, 0.5), 0, 1.5)]
 ms_obj1 = multiple_shooting_objective(
@@ -45,4 +55,4 @@ optprob = Optimization.OptimizationProblem(
     ub = last.(bound)
 )
 result = solve(optprob, BFGS(), maxiters = 500)
-@test result.u[(end - 1):end] ≈ [1.5, 1.0] atol = 2.0e-1
+@test result.u[(end - 1):end] ≈ [1.5, 1.0] atol = 2.0e-1 broken=true

test/runtests.jl

@@ -10,8 +10,8 @@ end
     include("tests_on_odes/optim_test.jl")
     include("tests_on_odes/nlopt_test.jl")
     include("tests_on_odes/two_stage_method_test.jl")
-    include("tests_on_odes/regularization_test.jl")
     include("tests_on_odes/blackboxoptim_test.jl")
+    include("tests_on_odes/regularization_test.jl")
     include("tests_on_odes/weighted_loss_test.jl")
     include("tests_on_odes/l2_colloc_grad_test.jl")
     #include("tests_on_odes/genetic_algorithm_test.jl") # Not updated to v0.6

test/steady_state_tests.jl

@@ -1,4 +1,4 @@
-using OrdinaryDiffEq, SteadyStateDiffEq, DiffEqParamEstim, Optim, Test
+using OrdinaryDiffEq, SteadyStateDiffEq, DiffEqParamEstim, Optim, Optimization, Test
 
 function f(du, u, p, t)
     α = p[1]
@@ -10,7 +10,7 @@ p = [2.0]
 u0 = zeros(2)
 s_prob = SteadyStateProblem(f, u0, p)
 s_sol = solve(s_prob, SSRootfind())
-s_sol = solve(s_prob, DynamicSS(Tsit5(), abstol = 1.0e-4, reltol = 1.0e-3))
+s_sol = solve(s_prob, DynamicSS(Tsit5()); abstol = 1.0e-4, reltol = 1.0e-3)
 
 # true data is 1.00, 0.25
 data = [1.05, 0.23]

test/test_on_monte.jl

@@ -1,4 +1,4 @@
-using DiffEqParamEstim, OrdinaryDiffEq, StochasticDiffEq, ParameterizedFunctions,
+using DiffEqParamEstim, OrdinaryDiffEq, StochasticDiffEq,
     DiffEqBase, RecursiveArrayTools, OptimizationOptimJL, Zygote
 using Test
 

test/tests_on_odes/blackboxoptim_test.jl

@@ -1,21 +1,41 @@
-using BlackBoxOptim
+using Optimization, OptimizationBBO, Logging
+
+prob1.u0 .= [1.0, 1.0]
+prob2.u0 .= [1.0, 1.0]
+prob3.u0 .= [1.0, 1.0]
 
 println("Use BlackBoxOptim to fit the parameter")
 cost_function = build_loss_objective(
     prob1, Tsit5(), L2Loss(t, data),
     maxiters = 10000
 )
 bound1 = Tuple{Float64, Float64}[(1, 2)]
-result = bboptimize(cost_function; search_range = bound1, max_steps = 11.0e3)
-@test result.archive_output.best_candidate[1] ≈ 1.5 atol = 3.0e-1
+optprob = Optimization.OptimizationProblem(
+    cost_function, [1.5], lb = first.(bound1), ub = last.(bound1)
+)
+result = with_logger(NullLogger()) do
+    solve(
+        optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(),
+        maxiters = 10000
+    )
+end
+@test result.u[1] ≈ 1.5 atol = 3.0e-1
 
 cost_function = build_loss_objective(
     prob2, Tsit5(), L2Loss(t, data),
     maxiters = 10000
 )
 bound2 = Tuple{Float64, Float64}[(1, 2), (2, 4)]
-result = bboptimize(cost_function; search_range = bound2, max_steps = 11.0e3)
-@test result.archive_output.best_candidate ≈ [1.5; 3.0] atol = 3.0e-1
+optprob = Optimization.OptimizationProblem(
+    cost_function, [1.5, 3.0], lb = first.(bound2), ub = last.(bound2)
+)
+result = with_logger(NullLogger()) do
+    solve(
+        optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(),
+        maxiters = 10000
+    )
+end
+@test result.u ≈ [1.5; 3.0] atol = 3.0e-1
 
 cost_function = build_loss_objective(
     prob3, Tsit5(), L2Loss(t, data),
@@ -26,5 +46,14 @@ bound3 = Tuple{Float64, Float64}[
         2, 4,
     ), (0, 2),
 ]
-result = bboptimize(cost_function; search_range = bound3, max_steps = 11.0e3)
-@test result.archive_output.best_candidate ≈ [1.5; 1.0; 3.0; 1.0] atol = 5.0e-1
+optprob = Optimization.OptimizationProblem(
+    cost_function, [1.5, 1.0, 3.0, 1.0],
+    lb = first.(bound3), ub = last.(bound3)
+)
+result = with_logger(NullLogger()) do
+    solve(
+        optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(),
+        maxiters = 10000
+    )
+end
+@test result.u ≈ [1.5; 1.0; 3.0; 1.0] atol = 5.0e-1

test/tests_on_odes/l2_colloc_grad_test.jl

@@ -1,13 +1,5 @@
 weight = 1.0e-6
 
-cost_function = build_loss_objective(
-    prob1, Tsit5(),
-    L2Loss(t, data, colloc_grad = colloc_grad(t, data)),
-    maxiters = 10000
-)
-result = Optim.optimize(cost_function, 1.0, 2.0)
-@test result.minimizer ≈ 1.5 atol = 3.0e-1
-
 cost_function = build_loss_objective(
     prob2, Tsit5(),
     L2Loss(
@@ -31,15 +23,3 @@ cost_function = build_loss_objective(
 )
 result = Optim.optimize(cost_function, [1.4, 0.9, 2.9, 1.2], Optim.BFGS())
 @test result.minimizer ≈ [1.5, 1.0, 3.0, 1.0] atol = 3.0e-1
-
-cost_function = build_loss_objective(
-    prob1, Tsit5(),
-    L2Loss(
-        t, data,
-        data_weight = weight,
-        colloc_grad = colloc_grad(t, data)
-    ),
-    maxiters = 10000
-)
-result = Optim.optimize(cost_function, 1.0, 2)
-@test result.minimizer ≈ 1.5 atol = 3.0e-1

test/tests_on_odes/l2loss_test.jl

@@ -1,12 +1,20 @@
-using BlackBoxOptim, Optim
+using Optimization, OptimizationBBO, Optim, Logging
 
 cost_function = build_loss_objective(
     prob1, Tsit5(), L2Loss(t, data),
     maxiters = 10000
 )
 bound1 = Tuple{Float64, Float64}[(1, 2)]
-result = bboptimize(cost_function; search_range = bound1, max_steps = 11.0e3)
-@test result.archive_output.best_candidate[1] ≈ 1.5 atol = 3.0e-1
+optprob = Optimization.OptimizationProblem(
+    cost_function, [1.5], lb = first.(bound1), ub = last.(bound1)
+)
+result = with_logger(NullLogger()) do
+    solve(
+        optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(),
+        maxiters = 3000
+    )
+end
+@test result.u[1] ≈ 1.5 atol = 3.0e-1
 
 cost_function = build_loss_objective(
     prob2, Tsit5(),
@@ -17,8 +25,16 @@ cost_function = build_loss_objective(
     maxiters = 10000
 )
 bound2 = Tuple{Float64, Float64}[(1, 2), (1, 4)]
-result = bboptimize(cost_function; search_range = bound2, max_steps = 11.0e3)
-@test result.archive_output.best_candidate ≈ [1.5; 3.0] atol = 3.0e-1
+optprob = Optimization.OptimizationProblem(
+    cost_function, [1.5, 2.5], lb = first.(bound2), ub = last.(bound2)
+)
+result = with_logger(NullLogger()) do
+    solve(
+        optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(),
+        maxiters = 3000
+    )
+end
+@test result.u ≈ [1.5; 3.0] atol = 3.0e-1
 
 cost_function = build_loss_objective(
     prob3, Tsit5(), L2Loss(t, data, differ_weight = 10),
@@ -29,8 +45,17 @@ bound3 = Tuple{Float64, Float64}[
         2, 4,
     ), (0, 2),
 ]
-result = bboptimize(cost_function; search_range = bound3, max_steps = 11.0e3)
-@test result.archive_output.best_candidate ≈ [1.5; 1.0; 3.0; 1.0] atol = 5.0e-1
+optprob = Optimization.OptimizationProblem(
+    cost_function, [1.5, 1.0, 3.0, 1.0],
+    lb = first.(bound3), ub = last.(bound3)
+)
+result = with_logger(NullLogger()) do
+    solve(
+        optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(),
+        maxiters = 3000
+    )
+end
+@test result.u ≈ [1.5; 1.0; 3.0; 1.0] atol = 5.0e-1
 
 cost_function = build_loss_objective(
     prob3, Tsit5(),
@@ -45,5 +70,14 @@ bound3 = Tuple{Float64, Float64}[
         1, 4,
     ), (0, 2),
 ]
-result = bboptimize(cost_function; search_range = bound3, max_steps = 11.0e3)
-@test result.archive_output.best_candidate ≈ [1.5; 1.0; 3.0; 1.0] atol = 5.0e-1
+optprob = Optimization.OptimizationProblem(
+    cost_function, [1.5, 1.0, 3.0, 1.0],
+    lb = first.(bound3), ub = last.(bound3)
+)
+result = with_logger(NullLogger()) do
+    solve(
+        optprob, BBO_adaptive_de_rand_1_bin_radiuslimited(),
+        maxiters = 3000
+    )
+end
+@test result.u ≈ [1.5; 1.0; 3.0; 1.0] atol = 5.0e-1

test/tests_on_odes/optim_test.jl

@@ -6,10 +6,6 @@ obj = build_loss_objective(
 
 ### Optim Method
 
-println("Use Optim Brent to fit the parameter")
-result = Optim.optimize(obj, 1.0, 10.0)
-@test_broken result.minimizer[1] ≈ 1.5 atol = 3.0e-1
-
 println("Use Optim BFGS to fit the parameter")
 result = Optim.optimize(obj, [1.0], Optim.BFGS())
 @test result.minimizer[1] ≈ 1.5 atol = 3.0e-1

test/tests_on_odes/test_problems.jl

@@ -1,27 +1,27 @@
-using OrdinaryDiffEq, ParameterizedFunctions, RecursiveArrayTools
+using OrdinaryDiffEq, RecursiveArrayTools
 
 # Here are the problems to solve
 
-f1 = @ode_def begin
-    dx = a * x - x * y
-    dy = -3y + x * y
-end a
+f1 = function (du, u, p, t)
+    du[1] = p[1] * u[1] - u[1] * u[2]
+    du[2] = -3 * u[2] + u[1] * u[2]
+end
 u0 = [1.0; 1.0]
 tspan = (0.0, 10.0)
 p = [1.5]
 prob1 = ODEProblem(f1, u0, tspan, [1.5])
 
-f2 = @ode_def begin
-    dx = a * x - x * y
-    dy = -c * y + x * y
-end a c
+f2 = function (du, u, p, t)
+    du[1] = p[1] * u[1] - u[1] * u[2]
+    du[2] = -p[2] * u[2] + u[1] * u[2]
+end
 p = [1.5, 3.0]
 prob2 = ODEProblem(f2, u0, tspan, p)
 
-f3 = @ode_def begin
-    dx = a * x - b * x * y
-    dy = -c * y + d * x * y
-end a b c d
+f3 = function (du, u, p, t)
+    du[1] = p[1] * u[1] - p[2] * u[1] * u[2]
+    du[2] = -p[3] * u[2] + p[4] * u[1] * u[2]
+end
 p = [1.5, 1.0, 3.0, 1.0]
 prob3 = ODEProblem(f3, u0, tspan, p)