Support for ComponentArrays

[1991jhf]

is it possible to support componet array for parameters?
if i have variable number of parameters, it will be diffucult to track their positions.
It will be nice if i make p0 a component array and get optimized param with same shape.

[lamont-granquist]

It seems to not be working for me:

using LsqFit
using ComponentArrays
using Parameters: @unpack

# solve J=1/2x^2+1/2y^2 subject to x + y = 2
function root!(z, ignored, u)
    println(u)
    @unpack x, y, λ = u
    z.∂L∂λ = x + y - 2.0
    z.∂L∂x = x - λ
    z.∂L∂y = y - λ
    println(z)
    return nothing
end

u0 = ComponentArray(x = 0.0, y = 0.0, λ = 0.0)
z = ComponentArray(∂L∂λ = 0.0, ∂L∂x = 0.0, ∂L∂y = 0.0)
sol = curve_fit(root!, [0.0], z, u0; inplace=true)

that throws in FiniteDiff.jl, I also get a failure by adding autodiff=:forwarddiff as well (which may be blowing up in LsqFit instead), I'm not sure which component it would be most useful to cut an issue against.

MethodError: no method matching reshape(::UnitRange{Int64}, ::Tuple{ComponentArrays.CombinedAxis{Axis{(x = 1, y = 2, λ = 3)}, Base.OneTo{Int64}}})

Closest candidates are:
  reshape(::AbstractVector, ::Colon)
   @ Base reshapedarray.jl:115
  reshape(::AbstractVector, ::Tuple{Colon})
   @ Base reshapedarray.jl:116
  reshape(::AbstractArray{T, N}, ::Val{N}) where {T, N}
   @ Base reshapedarray.jl:140
  ...


Stacktrace:
  [1] reshape(parent::UnitRange{Int64}, dims::ComponentArrays.CombinedAxis{Axis{(x = 1, y = 2, λ = 3)}, Base.OneTo{Int64}})
    @ Base ./reshapedarray.jl:110
  [2] finite_difference_jacobian!(J::ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, f::LsqFit.var"#17#19"{typeof(root!), Vector{Float64}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}}, x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, cache::FiniteDiff.JacobianCache{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, UnitRange{Int64}, Nothing, Val{:central}(), Float64}, f_in::Nothing; relstep::Float64, absstep::Float64, colorvec::UnitRange{Int64}, sparsity::Nothing, dir::Bool)
    @ FiniteDiff ~/.julia/packages/FiniteDiff/grio1/src/jacobians.jl:354
  [3] finite_difference_jacobian!(J::ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, f::Function, x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, cache::FiniteDiff.JacobianCache{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, UnitRange{Int64}, Nothing, Val{:central}(), Float64}, f_in::Nothing)
    @ FiniteDiff ~/.julia/packages/FiniteDiff/grio1/src/jacobians.jl:341
  [4] finite_difference_jacobian!(J::ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, f::Function, x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, cache::FiniteDiff.JacobianCache{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, UnitRange{Int64}, Nothing, Val{:central}(), Float64})
    @ FiniteDiff ~/.julia/packages/FiniteDiff/grio1/src/jacobians.jl:341
  [5] (::NLSolversBase.var"#fj_finitediff!#21"{LsqFit.var"#17#19"{typeof(root!), Vector{Float64}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}}, FiniteDiff.JacobianCache{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, UnitRange{Int64}, Nothing, Val{:central}(), Float64}})(F::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, J::ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}})
    @ NLSolversBase ~/.julia/packages/NLSolversBase/kavn7/src/objective_types/oncedifferentiable.jl:139
  [6] value_jacobian!!(obj::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, F::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, J::ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}})
    @ NLSolversBase ~/.julia/packages/NLSolversBase/kavn7/src/interface.jl:124
  [7] value_jacobian!!
    @ ~/.julia/packages/NLSolversBase/kavn7/src/interface.jl:122 [inlined]
  [8] levenberg_marquardt(df::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, initial_x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}; x_tol::Float64, g_tol::Float64, maxIter::Int64, lambda::Float64, tau::Float64, lambda_increase::Float64, lambda_decrease::Float64, min_step_quality::Float64, good_step_quality::Float64, show_trace::Bool, lower::Vector{Float64}, upper::Vector{Float64}, avv!::Nothing)
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/levenberg_marquardt.jl:42
  [9] levenberg_marquardt(df::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, initial_x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/levenberg_marquardt.jl:34
 [10] lmfit(R::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, wt::Vector{Float64}; autodiff::Symbol, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:68
 [11] lmfit(R::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, wt::Vector{Float64})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:67
 [12] lmfit(f::Function, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, wt::Vector{Float64}, r::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}; autodiff::Symbol, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:43
 [13] lmfit(f::Function, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, wt::Vector{Float64}, r::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:41
 [14] curve_fit(model::typeof(root!), xdata::Vector{Float64}, ydata::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}; inplace::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:115
 [15] top-level scope
    @ In[7]:1

[lamont-granquist]

The error when using ForwardDiff.jl:

ArgumentError: No method is implemented for reducing index range of type ComponentArrays.CombinedAxis{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Base.OneTo{Int64}}. Please implement
reduced_index for this index type or report this as an issue.


Stacktrace:
  [1] reduced_index(i::ComponentArrays.CombinedAxis{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Base.OneTo{Int64}})
    @ Base ./reducedim.jl:8
  [2] reduced_indices(inds::Tuple{ComponentArrays.CombinedAxis{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Base.OneTo{Int64}}, ComponentArrays.CombinedAxis{Axis{(x = 1, y = 2, λ = 3)}, Base.OneTo{Int64}}}, d::Int64)
    @ Base ./reducedim.jl:23
  [3] reduced_indices
    @ ./reducedim.jl:15 [inlined]
  [4] reducedim_initarray(A::ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, region::Int64, init::Float64, #unused#::Type{Float64})
    @ Base ./reducedim.jl:91
  [5] reducedim_initarray
    @ ./reducedim.jl:92 [inlined]
  [6] reducedim_init
    @ ./reducedim.jl:219 [inlined]
  [7] _mapreduce_dim
    @ ./reducedim.jl:371 [inlined]
  [8] #mapreduce#800
    @ ./reducedim.jl:357 [inlined]
  [9] mapreduce
    @ ./reducedim.jl:357 [inlined]
 [10] #_sum#834
    @ ./reducedim.jl:1023 [inlined]
 [11] _sum
    @ ./reducedim.jl:1023 [inlined]
 [12] #sum#808
    @ ./reducedim.jl:995 [inlined]
 [13] sum
    @ ./reducedim.jl:995 [inlined]
 [14] levenberg_marquardt(df::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, initial_x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}; x_tol::Float64, g_tol::Float64, maxIter::Int64, lambda::Float64, tau::Float64, lambda_increase::Float64, lambda_decrease::Float64, min_step_quality::Float64, good_step_quality::Float64, show_trace::Bool, lower::Vector{Float64}, upper::Vector{Float64}, avv!::Nothing)
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/levenberg_marquardt.jl:110
 [15] levenberg_marquardt(df::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, initial_x::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/levenberg_marquardt.jl:34
 [16] lmfit(R::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, wt::Vector{Float64}; autodiff::Symbol, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:68
 [17] lmfit(R::NLSolversBase.OnceDifferentiable{ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, ComponentMatrix{Float64, Matrix{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}, Axis{(x = 1, y = 2, λ = 3)}}}, ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}}, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, wt::Vector{Float64})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:67
 [18] lmfit(f::Function, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}, wt::Vector{Float64}, r::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}; autodiff::Symbol, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:43
 [19] curve_fit(model::typeof(root!), xdata::Vector{Float64}, ydata::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(∂L∂λ = 1, ∂L∂x = 2, ∂L∂y = 3)}}}, p0::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, λ = 3)}}}; inplace::Bool, kwargs::Base.Pairs{Symbol, Symbol, Tuple{Symbol}, NamedTuple{(:autodiff,), Tuple{Symbol}}})
    @ LsqFit ~/.julia/packages/LsqFit/BBrNp/src/curve_fit.jl:115
 [20] top-level scope
    @ In[6]:1

[pkofod]

is it possible to support componet array for parameters?
if i have variable number of parameters, it will be diffucult to track their positions.
It will be nice if i make p0 a component array and get optimized param with same shape.

If you give me an example of a model and the input you want to give, I can have a look.