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Incorrect x-axis limits for solution plot w/ function of states #63
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Yeah good point, that seems like it would be the correct thing to do. |
Actually there might be two ways to fix:
|
It would work. IIRC the reason why it was changed is because it gives no padding on the ends though. |
ChrisRackauckas
added a commit
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Jan 18, 2023
This should be a nice improvement overall to the health of the debugging experience. For example, the code from this post (https://discourse.julialang.org/t/optimizationmoi-ipopt-violating-inequality-constraint/92608) led to a question that took a bit to understand. But now when you run ```julia import Optimization import OptimizationMOI, Ipopt const AV{T} = AbstractVector{T} function model_constraints!(out::AV{<:Real}, u::AV{<:Real}, data) # Model parameters dt, a, b = u out[1] = a - 1/dt # Must be < 0 @info "Must be NEGATIVE: $(out[1])" end function model_variance(u::AV{T}, data::AV{<:Real}) where T<:Real # Model parameters dt, a, b = u # Compute variance variance = zeros(T, length(data)) variance[1] = one(T) for t in 1:(length(data) - 1) variance[t+1] = (1 - dt * a) * variance[t] + dt * data[t]^2 + dt * b end variance end function model_loss(u::AV{T}, data::AV{<:Real})::T where T<:Real variance = model_variance(u, data) loglik::T = zero(T) for (r, var) in zip(data, variance) loglik += -(log(2π) + log(var) + r^2 / var) / 2 end -loglik / length(data) end function model_fit(u0::AV{T}, data::AV{<:Real}) where T<:Real func = Optimization.OptimizationFunction( model_loss, Optimization.AutoForwardDiff(), cons=model_constraints! ) prob = Optimization.OptimizationProblem( func, u0, data, # 0 < dt < 1 && 1 < a < Inf && 0 < b < Inf lb=T[0.0, 1.0, 0.0], ub=T[1.0, Inf, Inf], # ^dt ^a ^b ^dt ^a ^b <= model parameters lcons=T[-Inf], ucons=T[0.0] # a - 1/dt < 0 ) sol = Optimization.solve(prob, Ipopt.Optimizer()) sol.u end let data = [ 2.1217711584057386, -0.28350145551002465, 2.3593492969513004, 0.192856733601849, 0.4566485836385113, 1.332717934013979, -1.286716619379847, 0.9868669960185211, 2.2358674776395224, -2.7933975791568098, 1.2555871497124622, 1.276879759908467, -0.8392016987911409, -1.1580875182201849, 0.33201646080578456, -0.17212553408696898, 1.1275285626369556, 0.23041139849229036, 1.648423577528424, 2.384823597473343, -0.4005518932539747, -1.117737311211693, -0.9490152960583265, -1.1454539355078672, 1.4158585811404159, -0.18926972177257692, -0.2867541528181491, -1.2077459688543788, -0.6397173049620141, 0.66147783407023, 0.049805188778543466, 0.902540117368457, -0.7018417933284938, 0.47342354473843684, 1.2620345361591596, -1.1483844812087018, -0.06487285080802752, 0.39020117013487715, -0.38454491504165356, 1.5125786171885645, -0.6751768274451174, 0.490916740658628, 0.012872300530924086, 0.46532447715746716, 0.34734421531357157, 0.3830452463549559, -0.8730874028738718, 0.4333151627834603, -0.40396180775692375, 2.0794821773418497, -0.5392735774960918, 0.6519326323752113, -1.4844713145398716, 0.3688828625691108, 1.010912990717231, 0.5018274939956874, 0.36656889279915833, -0.11403975693239479, -0.6460314660359935, -0.41997005020823147, 0.9652752515820495, -0.37375868692702047, -0.5780729659197872, 2.642742798278919, 0.5076984117208074, -0.4906395089461916, -1.804352047187329, -0.8596663844837792, -0.7510485548262176, -0.07922589350581195, 1.7201304839487317, 0.9024493222130577, -1.8216089665357902, 1.3929269238775426, -0.08410752079538407, 0.6423068180438288, 0.6615201016351212, 0.18546977816594887, -0.717521690742993, -1.0224309324751113, 1.7748350222721971, 0.1929546575877559, -0.1581871639724676, 0.20198379311238596, -0.6919373947349301, -0.9253274269423383, 0.549366272989534, -1.9302106783541606, 0.7197247279281573, -1.220334158468621, -0.9187468058921053, -2.1452607604834184, -2.1558650694862687, -0.9387913392336701, -0.676637835687265, -0.16621998352492198, 0.5637177022958897, -0.5258315560278541, 0.8413359958184765, -0.9096866525337141 ] # u0 = [0 < dt < 1, 1 < a < 1/dt, 0 < b < Inf] u0 = [0.3, 2.3333333333333335, 0.33333333333333337] @Assert 0 < u0[1] < 1 @Assert 1 < u0[2] < 1 / u0[1] @Assert 0 < u0[3] < Inf @info "Optimizing..." u0 model_fit(u0, data) end ``` you get: ```julia DomainError detected in the user `f` function. This occurs when the domain of a function is violated. For example, `log(-1.0)` is undefined because `log` of a real number is defined to only output real numbers, but `log` of a negative number is complex valued and therefore Julia throws a DomainError by default. Cases to be aware of include: * `log(x)`, `sqrt(x)`, `cbrt(x)`, etc. where `x<0` * `x^y` for `x<0` floating point `y` (example: `(-1.0)^(1/2) == im`) Within the context of SciML, this error can occur within the solver process even if the domain constraint would not be violated in the solution due to adaptivity. For example, an ODE solver or optimization routine may check a step at `new_u` which violates the domain constraint, and if violated reject the step and use a smaller `dt`. However, the throwing of this error will have halted the solving process. Thus the recommended fix is to replace this function with the equivalent ones from NaNMath.jl (https://github.com/JuliaMath/NaNMath.jl) which returns a NaN instead of an error. The solver will then effectively use the NaN within the error control routines to reject the out of bounds step. Additionally, one could perform a domain transformation on the variables so that such an issue does not occur in the definition of `f`. For more information, check out the following FAQ page: https://docs.sciml.ai/Optimization/stable/API/FAQ/#The-Solver-Seems-to-Violate-Constraints-During-the-Optimization,-Causing-DomainErrors,-What-Can-I-Do-About-That? Note that detailed debugging information adds a small amount of overhead to SciML solves which can be disabled with the keyword argument `debug = NoDebug()`. The detailed original error message information from Julia reproduced below: ERROR: DomainError with -2.4941978436429695: log will only return a complex result if called with a complex argument. Try log(Complex(x)). Stacktrace: [1] (::SciMLBase.VerboseDebugFunction{typeof(SciMLBase.__solve)})(::SciMLBase.OptimizationProblem{true, SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Nothing, Vector{Float64}, Vector{Float64}, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, ::Vararg{Any}) @ SciMLBase C:\Users\accou\.julia\dev\SciMLBase\src\debug.jl:66 [2] #solve#572 @ c:\Users\accou\.julia\dev\SciMLBase\src\solve.jl:87 [inlined] [3] solve @ c:\Users\accou\.julia\dev\SciMLBase\src\solve.jl:80 [inlined] [4] model_fit(u0::Vector{Float64}, data::Vector{Float64}) @ Main c:\Users\accou\OneDrive\Computer\Desktop\test.jl:77 [5] top-level scope @ c:\Users\accou\OneDrive\Computer\Desktop\test.jl:88 caused by: DomainError with -2.4941978436429695: log will only return a complex result if called with a complex argument. Try log(Complex(x)). Stacktrace: [1] throw_complex_domainerror(f::Symbol, x::Float64) @ Base.Math .\math.jl:33 [2] _log(x::Float64, base::Val{:ℯ}, func::Symbol) @ Base.Math .\special\log.jl:301 [3] log @ .\special\log.jl:267 [inlined] [4] model_loss(u::Vector{Float64}, data::Vector{Float64}) @ Main c:\Users\accou\OneDrive\Computer\Desktop\test.jl:60 [5] OptimizationFunction @ C:\Users\accou\.julia\dev\SciMLBase\src\scimlfunctions.jl:3580 [inlined] [6] eval_objective(moiproblem::OptimizationMOI.MOIOptimizationProblem{Float64, SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Optimization.var"#57#74"{ForwardDiff.GradientConfig{ForwardDiff.Tag{Optimization.var"#56#73"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}, Float64}, Float64, 3, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Optimization.var"#56#73"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}, Float64}, Float64, 3}}}, Optimization.var"#56#73"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}}, Optimization.var"#60#77"{ForwardDiff.HessianConfig{ForwardDiff.Tag{Optimization.var"#56#73"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}, Float64}, Float64, 3, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Optimization.var"#56#73"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}, Float64}, ForwardDiff.Dual{ForwardDiff.Tag{Optimization.var"#56#73"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}, Float64}, Float64, 3}, 3}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Optimization.var"#56#73"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}, Float64}, Float64, 3}}}, Optimization.var"#56#73"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}}, Optimization.var"#63#80", Optimization.var"#64#81"{SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}}, Optimization.var"#66#83"{ForwardDiff.JacobianConfig{ForwardDiff.Tag{Optimization.var"#65#82"{Int64}, Float64}, Float64, 3, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Optimization.var"#65#82"{Int64}, Float64}, Float64, 3}}}}, Optimization.var"#71#88"{Int64, Vector{ForwardDiff.HessianConfig{ForwardDiff.Tag{Optimization.var"#69#86"{Int64}, Float64}, Float64, 3, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Optimization.var"#69#86"{Int64}, Float64}, ForwardDiff.Dual{ForwardDiff.Tag{Optimization.var"#69#86"{Int64}, Float64}, Float64, 3}, 3}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Optimization.var"#69#86"{Int64}, Float64}, Float64, 3}}}}, Vector{Optimization.var"#69#86"{Int64}}}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, Vector{Float64}, Matrix{Float64}, Matrix{Float64}, Matrix{Float64}}, x::Vector{Float64}) @ OptimizationMOI C:\Users\accou\.julia\packages\OptimizationMOI\cHl7S\src\OptimizationMOI.jl:82 [7] eval_objective(model::Ipopt.Optimizer, x::Vector{Float64}) @ Ipopt C:\Users\accou\.julia\packages\Ipopt\rQctM\src\MOI_wrapper.jl:514 [8] (::Ipopt.var"#eval_f_cb#1"{Ipopt.Optimizer})(x::Vector{Float64}) @ Ipopt C:\Users\accou\.julia\packages\Ipopt\rQctM\src\MOI_wrapper.jl:597 [9] _Eval_F_CB(n::Int32, x_ptr::Ptr{Float64}, x_new::Int32, obj_value::Ptr{Float64}, user_data::Ptr{Nothing}) @ Ipopt C:\Users\accou\.julia\packages\Ipopt\rQctM\src\C_wrapper.jl:38 [10] IpoptSolve(prob::Ipopt.IpoptProblem) @ Ipopt C:\Users\accou\.julia\packages\Ipopt\rQctM\src\C_wrapper.jl:442 [11] optimize!(model::Ipopt.Optimizer) @ Ipopt C:\Users\accou\.julia\packages\Ipopt\rQctM\src\MOI_wrapper.jl:727 [12] __solve(prob::SciMLBase.OptimizationProblem{true, SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Nothing, Vector{Float64}, Vector{Float64}, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::Ipopt.Optimizer; maxiters::Nothing, maxtime::Nothing, abstol::Nothing, reltol::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ OptimizationMOI C:\Users\accou\.julia\packages\OptimizationMOI\cHl7S\src\OptimizationMOI.jl:381 [13] __solve(prob::SciMLBase.OptimizationProblem{true, SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Nothing, Vector{Float64}, Vector{Float64}, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::Ipopt.Optimizer) @ OptimizationMOI C:\Users\accou\.julia\packages\OptimizationMOI\cHl7S\src\OptimizationMOI.jl:327 [14] (::SciMLBase.VerboseDebugFunction{typeof(SciMLBase.__solve)})(::SciMLBase.OptimizationProblem{true, SciMLBase.OptimizationFunction{true, Optimization.AutoForwardDiff{nothing}, typeof(model_loss), Nothing, Nothing, Nothing, typeof(model_constraints!), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Nothing, Vector{Float64}, Vector{Float64}, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, ::Vararg{Any}) @ SciMLBase C:\Users\accou\.julia\dev\SciMLBase\src\debug.jl:59 [15] #solve#572 @ c:\Users\accou\.julia\dev\SciMLBase\src\solve.jl:87 [inlined] [16] solve @ c:\Users\accou\.julia\dev\SciMLBase\src\solve.jl:80 [inlined] [17] model_fit(u0::Vector{Float64}, data::Vector{Float64}) @ Main c:\Users\accou\OneDrive\Computer\Desktop\test.jl:77 [18] top-level scope @ c:\Users\accou\OneDrive\Computer\Desktop\test.jl:88 ```
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Description
When plotting an ODE solution using a function
f
such as invars = (f,2,3)
the x-axis is set on the range of the state variable corresponding to the first function argument, i.e.u[vars[2]]
. However, it should rather be set based on the range of the actually plotted values, which depend on the return value off
.Actual Behavior
Expected Behavior
Following statements should yield a plot with x-axis range [1.5, 2.2] and without axis label "t"
Related issues
OPEN: SciML/DiffEqBase.jl#591
CLOSED: SciML/DiffEqBase.jl#35
Possible solution
I pinpointed the behavior to the lines:
SciMLBase.jl/src/solutions/solution_interface.jl
Lines 204 to 212 in e44240f
Instead one could use
plot_vecs
, which acutally gets printed. Something along the lines below. However, I'm unsure whether this breaks other use cases.The text was updated successfully, but these errors were encountered: