Merge pull request #431 from SciML/qqy/tunable_parameters

Change fit_parameters to tune_parameters

Author: ChrisRackauckas

Project.toml

@@ -59,7 +59,7 @@ Random = "1.10"
 RecursiveArrayTools = "3.31.2"
 ReTestItems = "1.29"
 Reexport = "1.2"
-SciMLBase = "2.130.0"
+SciMLBase = "2.138.0"
 Sparspak = "0.3.11"
 StaticArrays = "1.9.8"
 Test = "1.10"

docs/src/tutorials/unknown_parameters.md

@@ -31,7 +31,7 @@ y_2(0)=0,\ y_2(\pi)=0
 
 It is worthnoting that in this system, while we have two differetial equations, it isn't enough to estimate the unknown parameters and guarantee a unique numerical solution with only two given boundary conditions. While under the hood, the parameters are estimated simultaneously with the numerical solution, it makes the boundary value problem an underconstrained BVP if the number of constraints are equal to the number of states, which may result in more than one solution. So we should provide additional constraint $y(0)=1$ from the original equation to make sure unique numerical solution and the estimated parameters are we actually wanted.
 
-With BoundaryValueDiffEq.jl, it's easy to solve boundary value problems with unknown parameters, we can just specify `fit_parameters=true` when constructing the BVP and provide the guess of the unknown parameters in `prob.p`, for example, to estimate the unknown parameters in the above BVP system:
+With BoundaryValueDiffEq.jl, it's easy to solve boundary value problems with unknown parameters, we can just specify `tune_parameters=true` when constructing the BVP and provide the guess of the unknown parameters in `prob.p`, for example, to estimate the unknown parameters in the above BVP system:
 
 ```@example unknown
 using BoundaryValueDiffEq, Plots
@@ -49,7 +49,7 @@ function bcb!(res, u, p)
 end
 guess(p, t) = [cos(4t); -4sin(4t)]
 bvp = TwoPointBVProblem(f!, (bca!, bcb!), guess, tspan, [15.0],
-    bcresid_prototype = (zeros(2), zeros(1)), fit_parameters = true)
+    bcresid_prototype = (zeros(2), zeros(1)), tune_parameters = true)
 sol = solve(bvp, MIRK4(), dt = 0.05)
 plot(sol)
 ```

lib/BoundaryValueDiffEqCore/Project.toml

@@ -45,7 +45,7 @@ OptimizationBase = "3.2.0, 4"
 PreallocationTools = "0.4.24"
 RecursiveArrayTools = "3.27.0"
 Reexport = "1.2"
-SciMLBase = "2.130.0"
+SciMLBase = "2.138.0"
 SciMLStructures = "1.7.0"
 Setfield = "1"
 SparseArrays = "1.10"

lib/BoundaryValueDiffEqCore/src/internal_problems.jl

@@ -1,9 +1,9 @@
 @inline __default_cost(::Nothing) = (x, p) -> 0.0
 @inline __default_cost(f) = f
 @inline __build_cost(::Nothing, cache, mesh, M; kwargs...) = (x, p) -> 0.0
-@inline function __build_cost(fun, cache, mesh, M; fit_parameters = false, p = nothing)
-    if fit_parameters && SciMLStructures.isscimlstructure(p)
-        # When fit_parameters=true, the state vector is augmented with tunable params
+@inline function __build_cost(fun, cache, mesh, M; tune_parameters = false, p = nothing)
+    if tune_parameters && SciMLStructures.isscimlstructure(p)
+        # When tune_parameters=true, the state vector is augmented with tunable params
         # Extract them and use SciMLStructures.replace to update p for the cost function
         tunable_part, _ = SciMLStructures.canonicalize(SciMLStructures.Tunable(), p)
         l_params = length(tunable_part)
@@ -16,10 +16,10 @@
             eval_sol = EvalSol(newy, mesh, cache)
             return fun(eval_sol, new_p)
         end
-    elseif fit_parameters && !isnothing(p)
+    elseif tune_parameters && !isnothing(p)
         length_u = M - length(p)
         cost_fun = @views function (u, p)
-            # When fit_parameters=true, the state vector is augmented with tunable params
+            # When tune_parameters=true, the state vector is augmented with tunable params
             newy = eachcol(reshape(u, M, :))
             params_from_u = u[(length_u + 1):M]
             eval_sol = EvalSol(newy, mesh, cache)

lib/BoundaryValueDiffEqCore/src/utils.jl

@@ -290,52 +290,52 @@ function __extract_problem_details(prob, u0::AbstractVectorOfArray; kwargs...)
 end
 function __extract_problem_details(
         prob, u0::AbstractArray; dt = 0.0,
-        check_positive_dt::Bool = false, fit_parameters::Bool = false
+        check_positive_dt::Bool = false, tune_parameters::Bool = false
     )
     # Problem does not have Initial Guess
     check_positive_dt && dt ≤ 0 && throw(ArgumentError("dt must be positive"))
     t₀, t₁ = prob.tspan
-    if fit_parameters
+    if tune_parameters
         prob.p isa SciMLBase.NullParameters &&
-            throw(ArgumentError("`fit_parameters` is true but `prob.p` is not set."))
+            throw(ArgumentError("`tune_parameters` is true but `prob.p` is not set."))
         new_u = vcat(u0, __tunable_part(prob.p))
         return Val(false), eltype(new_u), length(new_u), Int(cld(t₁ - t₀, dt)), new_u
     end
     return Val(false), eltype(u0), length(u0), Int(cld(t₁ - t₀, dt)), prob.u0
 end
 function __extract_problem_details(
         prob, f::F; dt = 0.0, check_positive_dt::Bool = false,
-        fit_parameters::Bool = false
+        tune_parameters::Bool = false
     ) where {F <: Function}
     # Problem passes in a initial guess function
     check_positive_dt && dt ≤ 0 && throw(ArgumentError("dt must be positive"))
 
-    u0 = __initial_guess(f, prob.p, prob.tspan[1]; fit_parameters = fit_parameters)
+    u0 = __initial_guess(f, prob.p, prob.tspan[1]; tune_parameters = tune_parameters)
     t₀, t₁ = prob.tspan
     return Val(true), eltype(u0), length(u0), Int(cld(t₁ - t₀, dt)), u0
 end
 
 function __extract_problem_details(
         prob, u0::SciMLBase.ODESolution; dt = 0.0,
-        check_positive_dt::Bool = false, fit_parameters::Bool = false
+        check_positive_dt::Bool = false, tune_parameters::Bool = false
     )
     # Problem passes in a initial guess function
     _u0 = first(u0.u)
     _t = u0.t
-    if fit_parameters
+    if tune_parameters
         prob.p isa SciMLBase.NullParameters &&
-            throw(ArgumentError("`fit_parameters` is true but `prob.p` is not set."))
+            throw(ArgumentError("`tune_parameters` is true but `prob.p` is not set."))
         new_u = vcat(_u0, __tunable_part(prob.p))
         return Val(false), eltype(new_u), length(new_u), Int(cld(t₁ - t₀, dt)), new_u
     end
     return Val(true), eltype(_u0), length(_u0), (length(_t) - 1), _u0
 end
 
-function __initial_guess(f::F, p::P, t::T; fit_parameters = false) where {F, P, T}
+function __initial_guess(f::F, p::P, t::T; tune_parameters = false) where {F, P, T}
     if hasmethod(f, Tuple{P, T})
-        if fit_parameters
+        if tune_parameters
             p isa SciMLBase.NullParameters &&
-                throw(ArgumentError("`fit_parameters` is true but `prob.p` is not set."))
+                throw(ArgumentError("`tune_parameters` is true but `prob.p` is not set."))
             return vcat(f(p, t), __tunable_part(p))
         end
         return f(p, t)
@@ -346,9 +346,9 @@ function __initial_guess(f::F, p::P, t::T; fit_parameters = false) where {F, P,
                      removed in the next major release of SciMLBase.",
             :__initial_guess
         )
-        if fit_parameters
+        if tune_parameters
             p isa SciMLBase.NullParameters &&
-                throw(ArgumentError("`fit_parameters` is true but `prob.p` is not set."))
+                throw(ArgumentError("`tune_parameters` is true but `prob.p` is not set."))
             return vcat(f(t), __tunable_part(p))
         end
         return f(t)

lib/BoundaryValueDiffEqFIRK/Project.toml

@@ -54,7 +54,7 @@ Random = "1.10"
 ReTestItems = "1.23.1"
 RecursiveArrayTools = "3.27.0"
 Reexport = "1.2"
-SciMLBase = "2.130.0"
+SciMLBase = "2.138.0"
 SciMLStructures = "1.7.0"
 Setfield = "1.1.1"
 SparseArrays = "1.10"