Merge pull request #294 from ChrisRackauckas-Claude/bump-ordinarydiffeq-v7-ecosystem

Bump compat for OrdinaryDiffEq v7 / SciMLBase v3 ecosystem

Author: ChrisRackauckas

Project.toml

@@ -20,17 +20,20 @@ StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 [compat]
 Calculus = "0.5"
 CommonSolve = "0.2.6"
+DelayDiffEq = "5, 6"
 Dierckx = "0.4, 0.5"
 DiffEqBase = "6, 7"
 Distributions = "0.25"
 ForwardDiff = "0.10"
+OrdinaryDiffEq = "6, 7"
 PenaltyFunctions = "0.1, 0.2, 0.3"
 PreallocationTools = "0.2, 0.3, 0.4, 1.0"
 RecursiveArrayTools = "1.0, 2.0, 3, 4"
 SciMLBase = "1.69, 2, 3"
 SciMLSensitivity = "7"
 Statistics = "1.10"
 StatsAPI = "1.8.0"
+StochasticDiffEq = "6, 7"
 julia = "1.6"
 
 [extras]

test/dae_tests.jl

@@ -22,15 +22,15 @@ using DiffEqParamEstim, OptimizationNLopt, OptimizationOptimJL, ForwardDiff, Zyg
 cost_function = build_loss_objective(
     prob, DFBDF(), L2Loss(t, data),
     Optimization.AutoZygote(), abstol = 1.0e-8,
-    reltol = 1.0e-8, verbose = false
+    reltol = 1.0e-8
 )
 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 = 1.0e-8,
-    reltol = 1.0e-8, verbose = false
+    reltol = 1.0e-8
 )
 optprob = Optimization.OptimizationProblem(cost_function, [0.01]; lb = [0.0], ub = [1.0])
 res = solve(optprob, OptimizationOptimJL.BFGS())

test/likelihood.jl

@@ -20,8 +20,7 @@ aggregate_data = convert(Array, VectorOfArray([generate_data(sol, t) for i in 1:
 
 distributions = [fit_mle(Normal, aggregate_data[i, j, :]) for i in 1:2, j in 1:200]
 obj = build_loss_objective(
-    prob1, Tsit5(), LogLikeLoss(t, distributions), maxiters = 10000,
-    verbose = false
+    prob1, Tsit5(), LogLikeLoss(t, distributions), maxiters = 10000
 )
 
 optprob = Optimization.OptimizationProblem(
@@ -42,8 +41,7 @@ diff_distributions = [
 obj = build_loss_objective(
     prob1, Tsit5(),
     LogLikeLoss(t, data_distributions, diff_distributions),
-    Optimization.AutoForwardDiff(), maxiters = 10000,
-    verbose = false
+    Optimization.AutoForwardDiff(), maxiters = 10000
 )
 optprob = Optimization.OptimizationProblem(
     obj, [2.0, 2.0], lb = [0.5, 0.5],
@@ -63,8 +61,7 @@ diff_distributions = [
 obj = build_loss_objective(
     prob1, Tsit5(),
     LogLikeLoss(t, data_distributions, diff_distributions, 0.3),
-    Optimization.AutoForwardDiff(), maxiters = 10000,
-    verbose = false
+    Optimization.AutoForwardDiff(), maxiters = 10000
 )
 optprob = Optimization.OptimizationProblem(
     obj, [2.0, 2.0], lb = [0.5, 0.5],
@@ -89,7 +86,7 @@ obj = build_loss_objective(
     prob1, Tsit5(),
     LogLikeLoss(t, distributions, diff_distributions),
     Optimization.AutoForwardDiff(), maxiters = 10000,
-    verbose = false, priors = priors
+    priors = priors
 )
 optprob = Optimization.OptimizationProblem(
     obj, [2.0, 2.0], lb = [0.5, 0.5],

test/out_of_place_odes.jl

@@ -26,7 +26,7 @@ soll = solve(prob, Tsit5())
 cost_function = build_loss_objective(
     prob, Tsit5(), L2Loss(t, data),
     Optimization.AutoZygote(),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 optprob = Optimization.OptimizationProblem(cost_function, [1.0], lb = [0.0], ub = [10.0])
 sol = solve(optprob, BFGS())

test/steady_state_tests.jl

@@ -18,7 +18,7 @@ obj = build_loss_objective(
     s_prob, SSRootfind(), L2Loss([Inf], data),
     Optimization.AutoZygote(),
     maxiters = Int(1.0e8),
-    abstol = 1.0e-10, reltol = 1.0e-10, verbose = true
+    abstol = 1.0e-10, reltol = 1.0e-10
 )
 result = Optim.optimize(obj, [2.0], Optim.BFGS())
 @test result.minimizer[1] ≈ 2.0 atol = 2.0e-1

test/test_on_monte.jl

@@ -22,7 +22,7 @@ obj = build_loss_objective(
     monte_prob, Tsit5(), L2Loss(t, data),
     Optimization.AutoForwardDiff(), maxiters = 10000,
     abstol = 1.0e-8, reltol = 1.0e-8,
-    verbose = false, trajectories = 25
+    trajectories = 25
 )
 optprob = Optimization.OptimizationProblem(obj, [1.3, 0.8])
 result = solve(optprob, Optim.BFGS())

test/tests_on_odes/blackboxoptim_test.jl

@@ -3,23 +3,23 @@ using BlackBoxOptim
 println("Use BlackBoxOptim to fit the parameter")
 cost_function = build_loss_objective(
     prob1, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false
+    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
 
 cost_function = build_loss_objective(
     prob2, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false
+    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
 
 cost_function = build_loss_objective(
     prob3, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 bound3 = Tuple{Float64, Float64}[
     (1, 2), (0, 2), (

test/tests_on_odes/genetic_algorithm_test.jl

@@ -15,7 +15,7 @@ println("Use Genetic Algorithm to fit the parameter")
 
 cost_function = build_loss_objective(
     prob1, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 N = 1
 result, fitness,

test/tests_on_odes/l2_colloc_grad_test.jl

@@ -3,7 +3,7 @@ weight = 1.0e-6
 cost_function = build_loss_objective(
     prob1, Tsit5(),
     L2Loss(t, data, colloc_grad = colloc_grad(t, data)),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 result = Optim.optimize(cost_function, 1.0, 2.0)
 @test result.minimizer ≈ 1.5 atol = 3.0e-1
@@ -15,7 +15,7 @@ cost_function = build_loss_objective(
         differ_weight = weight, data_weight = weight,
         colloc_grad = colloc_grad(t, data)
     ),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 result = Optim.optimize(cost_function, [1.3, 2.8], Optim.BFGS())
 @test result.minimizer ≈ [1.5; 3.0] atol = 3.0e-1
@@ -27,7 +27,7 @@ cost_function = build_loss_objective(
         differ_weight = weight,
         colloc_grad = colloc_grad(t, data)
     ),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 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
@@ -39,7 +39,7 @@ cost_function = build_loss_objective(
         data_weight = weight,
         colloc_grad = colloc_grad(t, data)
     ),
-    maxiters = 10000, verbose = false
+    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

@@ -2,7 +2,7 @@ using BlackBoxOptim, Optim
 
 cost_function = build_loss_objective(
     prob1, Tsit5(), L2Loss(t, data),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 bound1 = Tuple{Float64, Float64}[(1, 2)]
 result = bboptimize(cost_function; search_range = bound1, max_steps = 11.0e3)
@@ -14,15 +14,15 @@ cost_function = build_loss_objective(
         t, data, differ_weight = nothing,
         data_weight = 1.0
     ),
-    maxiters = 10000, verbose = false
+    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
 
 cost_function = build_loss_objective(
     prob3, Tsit5(), L2Loss(t, data, differ_weight = 10),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 bound3 = Tuple{Float64, Float64}[
     (1, 2), (0, 2), (
@@ -38,7 +38,7 @@ cost_function = build_loss_objective(
         t, data, differ_weight = 0.3,
         data_weight = 0.7
     ),
-    maxiters = 10000, verbose = false
+    maxiters = 10000
 )
 bound3 = Tuple{Float64, Float64}[
     (1, 2), (0, 2), (