Migrate to SciMLBase v3 / RecursiveArrayTools v4 / DiffEqBase v7

Project.toml

@@ -1,6 +1,6 @@
 name = "DiffEqParamEstim"
 uuid = "1130ab10-4a5a-5621-a13d-e4788d82bd4c"
-version = "2.4.0"
+version = "2.5.0"
 authors = ["Chris Rackauckas <accounts@chrisrackauckas.com>"]
 
 [deps]
@@ -21,13 +21,13 @@ StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 Calculus = "0.5"
 CommonSolve = "0.2.6"
 Dierckx = "0.4, 0.5"
-DiffEqBase = "6"
+DiffEqBase = "6, 7"
 Distributions = "0.25"
 ForwardDiff = "0.10"
 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, 3.1"
+SciMLBase = "1.69, 2, 3"
 SciMLSensitivity = "7"
 Statistics = "1.10"
 StatsAPI = "1.8.0"

src/cost_functions.jl

@@ -40,24 +40,25 @@ function (f::L2Loss)(sol::DiffEqBase.AbstractNoTimeSolution)
     dudt = f.dudt
 
     if sol isa DiffEqBase.AbstractEnsembleSolution
-        failure = any(!SciMLBase.successful_retcode(s.retcode) for s in sol)
+        failure = any(!SciMLBase.successful_retcode(s.retcode) for s in sol.u)
     else
         failure = !SciMLBase.successful_retcode(sol.retcode)
     end
     failure && return Inf
 
     sumsq = 0.0
 
+    solu = sol.u
     if weight === nothing
-        @inbounds for i in 1:length(sol)
-            sumsq += (data[i] - sol[i])^2
+        @inbounds for i in 1:length(solu)
+            sumsq += (data[i] - solu[i])^2
         end
     else
-        @inbounds for i in 1:length(sol)
+        @inbounds for i in 1:length(solu)
             if weight isa Real
-                sumsq = sumsq + ((data[i] - sol[i])^2) * weight
+                sumsq = sumsq + ((data[i] - solu[i])^2) * weight
             else
-                sumsq = sumsq + ((data[i] - sol[i])^2) * weight[i]
+                sumsq = sumsq + ((data[i] - solu[i])^2) * weight[i]
             end
         end
     end
@@ -72,16 +73,17 @@ function (f::L2Loss)(sol::SciMLBase.AbstractSciMLSolution)
     dudt = f.dudt
 
     if sol isa DiffEqBase.AbstractEnsembleSolution
-        failure = any(!SciMLBase.successful_retcode(s.retcode) for s in sol)
+        failure = any(!SciMLBase.successful_retcode(s.retcode) for s in sol.u)
     else
         failure = !SciMLBase.successful_retcode(sol.retcode)
     end
     failure && return Inf
 
     sumsq = 0.0
 
+    nsteps = length(sol.u)
     if weight === nothing
-        @inbounds for i in 1:length(sol)
+        @inbounds for i in 1:nsteps
             for j in 1:length(sol.u[i])
                 sumsq += (data[j, i] - sol[j, i])^2
             end
@@ -108,7 +110,7 @@ function (f::L2Loss)(sol::SciMLBase.AbstractSciMLSolution)
             end
         end
     else
-        @inbounds for i in 1:length(sol)
+        @inbounds for i in 1:nsteps
             if weight isa Real
                 for j in 1:length(sol.u[i])
                     sumsq = sumsq + ((data[j, i] - sol[j, i])^2) * weight
@@ -195,7 +197,7 @@ end
 function (f::LogLikeLoss)(sol::SciMLBase.AbstractSciMLSolution)
     distributions = f.data_distributions
     if sol isa DiffEqBase.AbstractEnsembleSolution
-        failure = any(!SciMLBase.successful_retcode(s.retcode) for s in sol)
+        failure = any(!SciMLBase.successful_retcode(s.retcode) for s in sol.u)
     else
         failure = !SciMLBase.successful_retcode(sol.retcode)
     end
@@ -244,23 +246,23 @@ end
 
 function (f::LogLikeLoss)(sol::DiffEqBase.AbstractEnsembleSolution)
     distributions = f.data_distributions
-    failure = any(!SciMLBase.successful_retcode(s.retcode) for s in sol)
+    failure = any(!SciMLBase.successful_retcode(s.retcode) for s in sol.u)
     failure && return Inf
     ll = 0.0
     if eltype(distributions) <: UnivariateDistribution
         for j in 1:length(f.t), i in 1:length(sol.u[1].u[1])
             # i is the number of time points
             # j is the size of the system
             # corresponds to distributions[i,j]
-            vals = [s[i, j] for s in sol]
+            vals = [s[i, j] for s in sol.u]
             ll -= loglikelihood(distributions[i, j], vals)
         end
     else
         for j in 1:length(f.t)
             # i is the number of time points
             # j is the size of the system
             # corresponds to distributions[i,j]
-            vals = [s[i, j] for i in 1:length(sol.u[1].u[1]), s in sol]
+            vals = [s[i, j] for i in 1:length(sol.u[1].u[1]), s in sol.u]
             ll -= loglikelihood(distributions[j], vals)
         end
     end
@@ -271,12 +273,12 @@ function (f::LogLikeLoss)(sol::DiffEqBase.AbstractEnsembleSolution)
         if eltype(distributions) <: UnivariateDistribution
             for j in 2:length(f.t), i in 1:length(sol.u[1].u[1])
 
-                vals = [s[i, j] - s[i, j - 1] for s in sol]
+                vals = [s[i, j] - s[i, j - 1] for s in sol.u]
                 fdll -= logpdf(distributions[j - 1, i], vals)[1]
             end
         else
             for j in 2:length(f.t)
-                vals = [s[i, j] - s[i, j - 1] for i in 1:length(sol.u[1].u[1]), s in sol]
+                vals = [s[i, j] - s[i, j - 1] for i in 1:length(sol.u[1].u[1]), s in sol.u]
                 fdll -= logpdf(distributions[j - 1], vals)[1]
             end
         end

test/tests_on_odes/regularization_test.jl

@@ -36,8 +36,8 @@ result = solve(optprob, Optim.BFGS())
 
 optprob = Optimization.OptimizationProblem(cost_function_2, [1.2, 2.7])
 result = solve(optprob, Optim.BFGS())
-@test result.minimizer ≈ [1.5; 3.0] atol = 3.0e-1
+@test result.u ≈ [1.5; 3.0] atol = 3.0e-1
 
 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 = 5.0e-1
+@test result.u ≈ [1.5; 1.0; 3.0; 1.0] atol = 5.0e-1