Update tests

Project.toml

@@ -32,10 +32,7 @@ julia = "1.6"
 [extras]
 BlackBoxOptim = "a134a8b2-14d6-55f6-9291-3336d3ab0209"
 DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
-DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
-FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
@@ -53,4 +50,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Test", "BlackBoxOptim", "DelayDiffEq", "LeastSquaresOptim", "NLopt", "Optim", "OrdinaryDiffEq", "ParameterizedFunctions", "Random", "StochasticDiffEq", "SteadyStateDiffEq"]
+test = ["Test", "BlackBoxOptim", "DelayDiffEq", "ForwardDiff", "NLopt", "Optim",  "Optimization", "OptimizationBBO", "OptimizationNLopt", "OptimizationOptimJL", "OrdinaryDiffEq", "ParameterizedFunctions", "Random", "SciMLSensitivity", "StochasticDiffEq", "SteadyStateDiffEq", "Sundials", "Zygote"]

test/dae_tests.jl

@@ -17,20 +17,21 @@ t = collect(range(0, stop = 10, length = 30))
 randomized = VectorOfArray([(sol(t[i]) + 0.003randn(3)) for i in 1:length(t)])
 data = convert(Array, randomized)
 
-using DiffEqParamEstim, OptimizationNLopt, OptimizationOptimJL, ForwardDiff, FiniteDiff,
- Zygote, Optimization, SciMLSensitivity
+using DiffEqParamEstim, OptimizationNLopt, OptimizationOptimJL, ForwardDiff, Zygote,
+ Optimization, SciMLSensitivity
 cost_function = build_loss_objective(prob, DFBDF(), L2Loss(t, data),
  Optimization.AutoZygote(), abstol = 1e-8,
  reltol = 1e-8, verbose = false)
-optprob = OptimizationProblem(cost_function, [0.01]; lb = [0.0], ub = [1.0])
+optprob = Optimization.OptimizationProblem(cost_function, [0.01]; lb = [0.0], ub = [1.0])
 res = solve(optprob, OptimizationOptimJL.BFGS())
 
 cost_function = build_loss_objective(prob, DFBDF(), L2Loss(t, data),
  Optimization.AutoForwardDiff(), abstol = 1e-8,
  reltol = 1e-8, verbose = false)
-optprob = OptimizationProblem(cost_function, [0.01]; lb = [0.0], ub = [1.0])
+optprob = Optimization.OptimizationProblem(cost_function, [0.01]; lb = [0.0], ub = [1.0])
 res = solve(optprob, OptimizationOptimJL.BFGS())
-opt = Opt(:GN_ESCH, 1)
+@test res.u[1]≈0.04 atol=5e-3
 
-res = solve(optprob, opt)
-# @test minx[1]≈0.04 atol=5e-3
+# opt = Opt(:GN_ESCH, 1)
+# res = solve(optprob, opt)
+# @test res.u[1]≈0.04 atol=5e-3

test/dde_tests.jl

@@ -33,8 +33,8 @@ cost_function = build_loss_objective(prob_opt, MethodOfSteps(Tsit5()),
  abstol = 1e-8,
  reltol = 1e-8)
 
-optprob = OptimizationProblem(cost_function, [1.0], lb = [0.0], ub = [1.0])
+optprob = Optimization.OptimizationProblem(cost_function, [1.0], lb = [0.0], ub = [1.0])
 opt = Opt(:GN_ESCH, 1)
 res = solve(optprob, BFGS())
 
-@test minx[1]≈0.5 atol=5e-3
+@test res.u[1]≈0.5 atol=5e-3

test/likelihood.jl

@@ -22,7 +22,8 @@ distributions = [fit_mle(Normal, aggregate_data[i, j, :]) for i in 1:2, j in 1:2
 obj = build_loss_objective(prob1, Tsit5(), LogLikeLoss(t, distributions), maxiters = 10000,
  verbose = false)
 
-optprob = OptimizationProblem(obj, [2.0, 2.0], lb = [0.5, 0.5], ub = [5.0, 5.0])
+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 = 11e3)
 @test result.original.archive_output.best_candidate≈[1.5, 1.0] atol=1e-1
 
@@ -34,7 +35,8 @@ obj = build_loss_objective(prob1, Tsit5(),
  LogLikeLoss(t, data_distributions, diff_distributions),
  Optimization.AutoForwardDiff(), maxiters = 10000,
  verbose = false)
-optprob = OptimizationProblem(obj, [2.0, 2.0], lb = [0.5, 0.5], ub = [5.0, 5.0])
+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 = 11e3)
 @test result.original.archive_output.best_candidate≈[1.5, 1.0] atol=1e-1
 
@@ -46,10 +48,12 @@ obj = build_loss_objective(prob1, Tsit5(),
  LogLikeLoss(t, data_distributions, diff_distributions, 0.3),
  Optimization.AutoForwardDiff(), maxiters = 10000,
  verbose = false)
-optprob = OptimizationProblem(obj, [2.0, 2.0], lb = [0.5, 0.5], ub = [5.0, 5.0])
+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 = 11e3)
+@test result.u≈[1.5, 1.0] atol=1e-1
 using OptimizationBBO.BlackBoxOptim
-bboptimize((x) -> obj(x, nothing), SearchRange = bound1)
+result = bboptimize(obj, SearchRange = [(0.5, 5.0), (0.5, 5.0)], MaxSteps = 11e3)
 @test result.archive_output.best_candidate≈[1.5, 1.0] atol=1e-1
 
 distributions = [fit_mle(MvNormal, aggregate_data[:, j, :]) for j in 1:200]
@@ -61,6 +65,7 @@ obj = build_loss_objective(prob1, Tsit5(),
  LogLikeLoss(t, distributions, diff_distributions),
  Optimization.AutoForwardDiff(), maxiters = 10000,
  verbose = false, priors = priors)
-optprob = OptimizationProblem(obj, [2.0, 2.0], lb = [0.5, 0.5], ub = [5.0, 5.0])
+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 = 11e3)
-@test result.archive_output.best_candidate≈[1.5, 1.0] atol=1e-1
+@test result.u≈[1.5, 1.0] atol=1e-1

test/out_of_place_odes.jl

@@ -1,4 +1,4 @@
-using OrdinaryDiffEq, Test, SciMLSensitivity
+using OrdinaryDiffEq, Test, SciMLSensitivity, Optimization, OptimizationOptimJL
 
 function LotkaVolterraTest_not_inplace(u, a, t)
  b, c, d = 1.0, 3.0, 1.0
@@ -26,7 +26,7 @@ soll = solve(prob, Tsit5())
 cost_function = build_loss_objective(prob, Tsit5(), L2Loss(t, data),
  Optimization.AutoZygote(),
  maxiters = 10000, verbose = false)
-optprob = OptimizationProblem(cost_function, [1.0], lb = [0.0], ub = [10.0])
+optprob = Optimization.OptimizationProblem(cost_function, [1.0], lb = [0.0], ub = [10.0])
 sol = solve(optprob, BFGS())
 
 # two-stage OOP regression test

test/test_on_monte.jl

@@ -1,5 +1,5 @@
 using DiffEqParamEstim, OrdinaryDiffEq, StochasticDiffEq, ParameterizedFunctions,
- DiffEqBase, RecursiveArrayTools
+ DiffEqBase, RecursiveArrayTools, OptimizationOptimJL, Zygote
 using Test
 
 pf_func = function (du, u, p, t)
@@ -18,12 +18,12 @@ randomized = VectorOfArray([(sol(t[i]) + 0.01randn(2)) for i in 1:length(t)])
 data = convert(Array, randomized)
 
 monte_prob = EnsembleProblem(prob)
-obj = build_loss_objective(monte_prob, Tsit5(), L2Loss(t, data), maxiters = 10000,
- Optimization.AutoZygote(),
+obj = build_loss_objective(monte_prob, Tsit5(), L2Loss(t, data),
+ Optimization.AutoForwardDiff(), maxiters = 10000,
  abstol = 1e-8, reltol = 1e-8,
  verbose = false, trajectories = 25)
-optprob = OptimizationProblem(obj, [1.3, 0.8])
-result = solve(obj, Optim.BFGS())
+optprob = Optimization.OptimizationProblem(obj, [1.3, 0.8])
+result = solve(optprob, Optim.BFGS())
 @test result.u≈[1.5, 1.0] atol=3e-1
 
 pg_func = function (du, u, p, t)

test/tests_on_odes/nlopt_test.jl

@@ -1,4 +1,4 @@
-using OptimizationNLopt
+using OptimizationNLopt, Zygote
 
 println("Use NLOpt to fit the parameter")
 
@@ -28,15 +28,11 @@ res = solve(optprob, opt)
 
 # test differentiation
 
-for adtype in (Optimization.AutoZygote(), SciMLBase.NoAD())
- global obj = build_loss_objective(prob1, Tsit5(), L2Loss(t, data), adtype;
- maxiters = 10000)
-
- global opt = Opt(:LD_MMA, 1)
- xtol_rel!(opt, 1e-3)
- maxeval!(opt, 10000)
- global minf, minx, ret
- optprob = OptimizationNLopt.OptimizationProblem(obj, [1.4])
- res = solve(optprob, opt)
- @test res.u[1]≈1.5 atol=1e-1
-end
+obj = build_loss_objective(prob1, Tsit5(), L2Loss(t, data), Optimization.AutoForwardDiff();
+ maxiters = 10000) #zygote behaves weirdly here
+opt = Opt(:LD_MMA, 1)
+xtol_rel!(opt, 1e-3)
+maxeval!(opt, 10000)
+optprob = OptimizationNLopt.OptimizationProblem(obj, [1.3])
+res = solve(optprob, opt)
+@test res.u[1]≈1.5 atol=1e-1

test/tests_on_odes/regularization_test.jl

@@ -1,4 +1,4 @@
-using PenaltyFunctions, OptimizationOptimJL, LinearAlgebra
+using PenaltyFunctions, OptimizationOptimJL, LinearAlgebra, SciMLSensitivity
 
 cost_function_1 = build_loss_objective(prob1, Tsit5(), L2Loss(t, data),
  Optimization.AutoZygote(),
@@ -20,14 +20,14 @@ cost_function_3 = build_loss_objective(prob3, Tsit5(), L2Loss(t, data),
  maxiters = 10000)
 
 println("Use Optim BFGS to fit the parameter")
-optprob = OptimizationNLopt.OptimizationProblem(cost_function_1, [1.0])
+optprob = Optimization.OptimizationProblem(cost_function_1, [1.0])
 result = solve(optprob, Optim.BFGS())
 @test result.u[1]≈1.5 atol=3e-1
 
-optprob = OptimizationProblem(cost_function_2, [1.2, 2.7])
+optprob = Optimization.OptimizationProblem(cost_function_2, [1.2, 2.7])
 result = solve(optprob, Optim.BFGS())
 @test result.minimizer≈[1.5; 3.0] atol=3e-1
 
-optprob = OptimizationProblem(cost_function_3, [1.3, 0.8, 2.8, 1.2])
+optprob = Optimization.OptimizationProblem(cost_function_3, [1.3, 0.8, 2.8, 1.2])
 result = solve(optprob, Optim.BFGS())
 @test result.minimizer≈[1.5; 1.0; 3.0; 1.0] atol=5e-1

test/tests_on_odes/test_problems.jl

@@ -1,4 +1,4 @@
-using OrdinaryDiffEq, ParameterizedFunctions, DiffEqBase, RecursiveArrayTools
+using OrdinaryDiffEq, ParameterizedFunctions, RecursiveArrayTools
 
 # Here are the problems to solve
 

test/tests_on_odes/two_stage_method_test.jl

@@ -1,4 +1,4 @@
-using Optim, NLopt
+using Optim, NLopt, OptimizationOptimJL
 
 println("Use Two Stage Method to fit the parameter")