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remove duplicate code for collocation #180

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remove duplicate code for collocation #180

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2 changes: 2 additions & 0 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@ version = "1.26.0"
Calculus = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
Dierckx = "39dd38d3-220a-591b-8e3c-4c3a8c710a94"
DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Expand All @@ -21,6 +22,7 @@ SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
Calculus = "0.5"
Dierckx = "0.4, 0.5"
DiffEqBase = "6"
DiffEqFlux = "1"
Distributions = "0.25"
ForwardDiff = "0.10"
LsqFit = "0.8, 0.9, 0.10, 0.11, 0.12"
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2 changes: 1 addition & 1 deletion docs/src/methods/collocation_loss.md
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Expand Up @@ -10,7 +10,7 @@ but is much faster, and is a good method to try first to get in the general
"good parameter" region, to then finish using one of the other methods.

```julia
function two_stage_method(prob::DEProblem,tpoints,data;kernel= :Epanechnikov,
function two_stage_method(prob::DEProblem,tpoints,data;kernel= EpanechnikovKernel(),
loss_func = L2DistLoss,mpg_autodiff = false,
verbose = false,verbose_steps = 100)
```
5 changes: 2 additions & 3 deletions src/DiffEqParamEstim.jl
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Original file line number Diff line number Diff line change
@@ -1,9 +1,9 @@
module DiffEqParamEstim
using DiffEqBase, LsqFit, PenaltyFunctions,
RecursiveArrayTools, ForwardDiff, Calculus, Distributions,
LinearAlgebra, SciMLSensitivity, Dierckx,
SciMLBase
LinearAlgebra, SciMLSensitivity, Dierckx, DiffEqFlux, SciMLBase

import DiffEqFlux.CollocationKernel
import PreallocationTools
STANDARD_PROB_GENERATOR(prob, p) = remake(prob; u0 = eltype(p).(prob.u0), p = p)
function STANDARD_PROB_GENERATOR(prob::EnsembleProblem, p)
Expand All @@ -26,7 +26,6 @@ include("cost_functions.jl")
include("lm_fit.jl")
include("build_loss_objective.jl")
include("build_lsoptim_objective.jl")
include("kernels.jl")
include("two_stage_method.jl")
include("multiple_shooting_objective.jl")

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86 changes: 0 additions & 86 deletions src/kernels.jl
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66 changes: 20 additions & 46 deletions src/two_stage_method.jl
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Original file line number Diff line number Diff line change
@@ -1,4 +1,7 @@
export TwoStageCost, two_stage_method
export EpanechnikovKernel, UniformKernel, TriangularKernel, QuarticKernel
export TriweightKernel, TricubeKernel, GaussianKernel, CosineKernel
export LogisticKernel, SigmoidKernel, SilvermanKernel

struct TwoStageCost{F, F2, D} <: Function
cost_function::F
Expand All @@ -10,7 +13,6 @@ end
(f::TwoStageCost)(p) = f.cost_function(p)
(f::TwoStageCost)(p, g) = f.cost_function2(p, g)

decide_kernel(kernel::CollocationKernel) = kernel
function decide_kernel(kernel::Symbol)
if kernel == :Epanechnikov
return EpanechnikovKernel()
Expand All @@ -32,51 +34,19 @@ function decide_kernel(kernel::Symbol)
return LogisticKernel()
elseif kernel == :Sigmoid
return SigmoidKernel()
else
elseif kernel == :Silverman
return SilvermanKernel()
else
return error("Kernel name not recognized")
end
end

function construct_t1(t, tpoints)
hcat(ones(eltype(tpoints), length(tpoints)), tpoints .- t)
end
function construct_t2(t, tpoints)
hcat(ones(eltype(tpoints), length(tpoints)), tpoints .- t, (tpoints .- t) .^ 2)
end
function construct_w(t, tpoints, h, kernel)
W = @. calckernel((kernel,), (tpoints - t) / h) / h
Diagonal(W)
end
function construct_estimated_solution_and_derivative!(data, kernel, tpoints)
_one = oneunit(first(data))
_zero = zero(first(data))
e1 = [_one; _zero]
e2 = [_zero; _one; _zero]
n = length(tpoints)
h = (n^(-1 / 5)) * (n^(-3 / 35)) * ((log(n))^(-1 / 16))

Wd = similar(data, n, size(data, 1))
WT1 = similar(data, n, 2)
WT2 = similar(data, n, 3)
x = map(tpoints) do _t
T1 = construct_t1(_t, tpoints)
T2 = construct_t2(_t, tpoints)
W = construct_w(_t, tpoints, h, kernel)
mul!(Wd, W, data')
mul!(WT1, W, T1)
mul!(WT2, W, T2)
(e2' * ((T2' * WT2) \ T2')) * Wd, (e1' * ((T1' * WT1) \ T1')) * Wd
end
estimated_derivative = reduce(hcat, transpose.(first.(x)))
estimated_solution = reduce(hcat, transpose.(last.(x)))
estimated_derivative, estimated_solution
end
function construct_iip_cost_function(f, du, preview_est_sol, preview_est_deriv, tpoints)
function (p)
_du = PreallocationTools.get_tmp(du, p)
vecdu = vec(_du)
cost = zero(first(p))
for i in 1:length(preview_est_sol)
for i in eachindex(preview_est_sol)
est_sol = preview_est_sol[i]
f(_du, est_sol, p, tpoints[i])
vecdu .= vec(preview_est_deriv[i]) .- vec(_du)
Expand All @@ -89,7 +59,7 @@ end
function construct_oop_cost_function(f, du, preview_est_sol, preview_est_deriv, tpoints)
function (p)
cost = zero(first(p))
for i in 1:length(preview_est_sol)
for i in eachindex(preview_est_sol)
est_sol = preview_est_sol[i]
_du = f(est_sol, p, tpoints[i])
cost += sum(abs2, vec(preview_est_deriv[i]) .- vec(_du))
Expand All @@ -102,23 +72,27 @@ get_chunksize(cs) = cs
get_chunksize(cs::Type{Val{CS}}) where {CS} = CS

function two_stage_method(prob::DiffEqBase.DEProblem, tpoints, data;
kernel = EpanechnikovKernel(),
kernel::Union{CollocationKernel, Symbol} = EpanechnikovKernel(),
loss_func = L2Loss, mpg_autodiff = false,
verbose = false, verbose_steps = 100,
autodiff_chunk = length(prob.p))
f = prob.f
kernel_function = decide_kernel(kernel)
estimated_derivative, estimated_solution = construct_estimated_solution_and_derivative!(data,
kernel_function,
tpoints)
if kernel isa Symbol
@warn "Passing kernels as Symbols will be deprecated"
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@Vaibhavdixit02 Vaibhavdixit02 Oct 15, 2022 •
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You can use @deprecate here

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I wanted to do it like that at first, but I ran into issues because Julia cannot dispatch on a keyword argument, i.e. it is impossible to have two methods, one with kernel restricted to Symbol types and one with kernel restricted to CollocationKernel, and then @deprecate the Symbol method.

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I don't think you can @depreacate here, it needs this special handling. But I also don't think we should be merging deprecations to this repo: it's somewhat in freeze mode because its interface does not extend well to more cases. In that case, major dependency changes and deprecations for an old repo not getting any major benefits is not a great feel. I think this PR should just hold until there's a good reason found for it.

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Yeah without kwarg handling it's not obvious, I think the way to do it with @deprecate would be
(though it's not relevant anymore since we won't go ahead with the PR, but it might be useful later)

my_func(args...; kwarg_to_deprecate::Union{T1,T2}, kwargs...) = my_func(args..., kwarg_to_deprecate; kwargs...)

my_func(args..., kwarg_to_deprecate::T1; kwargs....) = do something...

@deprecate my_func(args..., kwarg_to_deprecate::T2; kwargs...) = do something....

kernel = decide_kernel(kernel)
end

# Step - 1

estimated_derivative, estimated_solution = collocate_data(data, tpoints, kernel)

# Step - 2

du = PreallocationTools.dualcache(similar(prob.u0), autodiff_chunk)
preview_est_sol = [@view estimated_solution[:, i]
for i in 1:size(estimated_solution, 2)]
for i in axes(estimated_solution, 2)]
preview_est_deriv = [@view estimated_derivative[:, i]
for i in 1:size(estimated_solution, 2)]
for i in axes(estimated_solution, 2)]
f = prob.f
if DiffEqBase.isinplace(prob)
cost_function = construct_iip_cost_function(f, du, preview_est_sol,
preview_est_deriv, tpoints)
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4 changes: 3 additions & 1 deletion test/out_of_place_odes.jl
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Expand Up @@ -46,7 +46,9 @@ prob_oop = ODEProblem{false}(ff, u0, tspan, ps)
data = Array(solve(prob, Tsit5(), saveat = t))
ptest = ones(rc)

obj_ts = two_stage_method(prob, t, data; kernel = :Sigmoid)
obj_ts = two_stage_method(prob, t, data; kernel = SigmoidKernel())
@test obj_ts(ptest) ≈ 418.3400017500223^2
obj_ts = two_stage_method(prob_oop, t, data; kernel = SigmoidKernel())
@test obj_ts(ptest) ≈ 418.3400017500223^2
obj_ts = two_stage_method(prob_oop, t, data; kernel = :Sigmoid)
@test obj_ts(ptest) ≈ 418.3400017500223^2