SciML / ModelingToolkit.jl

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
https://mtk.sciml.ai/dev/
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modelingtoolkitize fails on ODEProblem where LHS is R code #2735

Open sdwfrost opened 6 months ago

sdwfrost commented 6 months ago

Describe the bug šŸž

I am trying to convert an ODEProblem that uses R code for the vector field to Julia using modelingtoolkitize. However, this throws an error due to no method matching sexpclass(::Num).

Expected behavior

The function should return an ODESystem.

Minimal Reproducible Example šŸ‘‡

using OrdinaryDiffEq
using ModelingToolkit
using RCall

R"""
sir_ode_r <- function(u,p,t){
    S <- u[1]
    I <- u[2]
    R <- u[3]
    N <- S+I+R
    beta <- p[1]
    cee <- p[2]
    gamma <- p[3]
    dS <- -beta*cee*I/N*S
    dI <- beta*cee*I/N*S - gamma*I
    dR <- gamma*I
    return(c(dS,dI,dR))
}
"""

function sir_ode_jl(u,p,t)
    robj = rcall(:sir_ode_r, u, p, t)
    return convert(Array,robj)
end

Ī“t = 0.1
tmax = 40.0
tspan = (0.0,tmax)
u0 = [990.0,10.0,0.0] # S,I,R
p = [0.05,10.0,0.25] # Ī²,c,Ī³
prob = ODEProblem{false}(sir_ode_jl, u0, tspan, p)
@named sys = modelingtoolkitize(prob)
# The above fails
prob_mtk = ODEProblem(sys, u0, tspan, p)
sol_mtk = solve(prob_mtk, Tsit5(), dt = Ī“t)

Error & Stacktrace āš ļø

ERROR: MethodError: no method matching sexpclass(::Num)

Closest candidates are:
  sexpclass(::SubArray{<:Any, <:Any, <:CategoricalArrays.CategoricalArray})
   @ RCall ~/.julia/packages/RCall/dDAVd/src/convert/default.jl:271
  sexpclass(::Tuple)
   @ RCall ~/.julia/packages/RCall/dDAVd/src/convert/tuple.jl:25
  sexpclass(::AbstractDict)
   @ RCall ~/.julia/packages/RCall/dDAVd/src/convert/default.jl:250
  ...

Stacktrace:
  [1] sexp(s::Num)
    @ RCall ~/.julia/packages/RCall/dDAVd/src/convert/default.jl:214
  [2] setindex!(s::Ptr{VecSxp}, value::Num, key::Int64)
    @ RCall ~/.julia/packages/RCall/dDAVd/src/methods.jl:195
  [3] sexp(#unused#::Type{RCall.RClass{:list}}, a::Vector{Num})
    @ RCall ~/.julia/packages/RCall/dDAVd/src/convert/base.jl:300
  [4] sexp(s::Vector{Num})
    @ RCall ~/.julia/packages/RCall/dDAVd/src/convert/default.jl:214
  [5] rlang_p(::Symbol, ::Vector{Num}, ::Vararg{Any}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ RCall ~/.julia/packages/RCall/dDAVd/src/language.jl:9
  [6] rlang_p(::Symbol, ::Vector{Num}, ::Vararg{Any})
    @ RCall ~/.julia/packages/RCall/dDAVd/src/language.jl:2
  [7] rcall_p(::Symbol, ::Vector{Num}, ::Vararg{Any}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ RCall ~/.julia/packages/RCall/dDAVd/src/language.jl:30
  [8] rcall_p(::Symbol, ::Vector{Num}, ::Vararg{Any})
    @ RCall ~/.julia/packages/RCall/dDAVd/src/language.jl:30
  [9] rcall(::Symbol, ::Vector{Num}, ::Vararg{Any}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ RCall ~/.julia/packages/RCall/dDAVd/src/language.jl:37
 [10] rcall
    @ ~/.julia/packages/RCall/dDAVd/src/language.jl:37 [inlined]
 [11] sir_ode_jl(u::Vector{Num}, p::Vector{Num}, t::Num)
    @ Main ~/Projects/github/ode_rcall3.jl:23
 [12] (::ODEFunction{false, SciMLBase.AutoSpecialize, typeof(sir_ode_jl), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing})(::Vector{Num}, ::Vararg{Any})
    @ SciMLBase ~/.julia/packages/SciMLBase/szsYq/src/scimlfunctions.jl:2266
 [13] modelingtoolkitize(prob::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, typeof(sir_ode_jl), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}; kwargs::Base.Pairs{Symbol, Symbol, Tuple{Symbol}, NamedTuple{(:name,), Tuple{Symbol}}})
    @ ModelingToolkit ~/.julia/packages/ModelingToolkit/jCQlF/src/systems/diffeqs/modelingtoolkitize.jl:55
 [14] top-level scope
    @ ~/.julia/packages/ModelingToolkit/jCQlF/src/systems/abstractsystem.jl:953

Environment (please complete the following information):

Status `~/.julia/environments/v1.9/Project.toml`
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āŒ… [961ee093] ModelingToolkit v8.46.1
āŒ… [315f7962] NeuralPDE v5.6.0
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āŒƒ [7f7a1694] Optimization v3.15.2
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āŒ… [9672c7b4] SteadyStateDiffEq v1.16.1
āŒ… [8254be44] SymbolicRegression v0.14.5
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  [44d3d7a6] Weave v0.10.12
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Julia Version 1.9.0
Commit 8e630552924 (2023-05-07 11:25 UTC)
Platform Info:
  OS: macOS (arm64-apple-darwin22.4.0)
  CPU: 10 Ɨ Apple M1 Max
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, apple-m1)
  Threads: 1 on 8 virtual cores
Environment:
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 1

Additional context

The PythonCall version of this code works as expected.

using OrdinaryDiffEq
using ModelingToolkit
using PythonCall

@pyexec """
def sir_ode_py(u,p,t):
    S = u[0]
    I = u[1]
    R = u[2]
    N = S+I+R
    beta = p[0]
    c = p[1]
    gamma = p[2]
    dS = -beta*c*I/N*S
    dI = beta*c*I/N*S - gamma*I
    dR = gamma*I
    return [dS,dI,dR]
""" => sir_ode_py

sir_ode_jl(u,p,t) = pyconvert(Array, sir_ode_py(u, p, t))

Ī“t = 0.1
tmax = 40.0
tspan = (0.0,tmax)
u0 = [990.0,10.0,0.0] # S,I,R
p = [0.05,10.0,0.25] # Ī²,c,Ī³
prob = ODEProblem{false}(sir_ode_jl, u0, tspan, p)
@named sys = modelingtoolkitize(prob)
prob_mtk = ODEProblem(sys, u0, tspan, p)
sol_mtk = solve(prob_mtk, Tsit5(), dt = Ī“t)
ChrisRackauckas commented 6 months ago

Good to have this documented but I don't expect someone to fix it any time soon. Looks deep in RCall. Not sure how JuliaCall fixes it.

sdwfrost commented 6 months ago

On the surface, it looks like it's just because the types from Symbolics.jl aren't wrapped (in this case, Num). I thought that it would work OOTB as diffeqr works ...

ChrisRackauckas commented 5 months ago

I thought it too would work the same way as diffeqr, so I'm a bit puzzled as to why RCall doesn't auto-wrap the same.

sdwfrost commented 5 months ago

This code doesn't throw an error, but it gets stuck on modelingtoolkitize. I'm not sure whether I'm wrapping wrong, or whether ModelingToolkit is just having a really hard time:

using OrdinaryDiffEq
using ModelingToolkit
using Symbolics
using RCall

RCall.sexp(::Type{RCall.Sxp}, x::Num) = RCall.sexp(x.val)
RCall.sexpclass(::Num) = RCall.Sxp
RCall.sexp(::Type{RCall.Sxp}, x::Term{Real, Base.ImmutableDict{DataType, Any}}) = RCall.sexp(eval(x))
RCall.sexpclass(::Term{Real, Base.ImmutableDict{DataType, Any}}) = RCall.Sxp

R"""
sir_ode_r <- function(u,p,t){
    S <- u[1]
    I <- u[2]
    R <- u[3]
    N <- S+I+R
    beta <- p[1]
    cee <- p[2]
    gamma <- p[3]
    dS <- -beta*cee*I/N*S
    dI <- beta*cee*I/N*S - gamma*I
    dR <- gamma*I
    return(c(dS,dI,dR))
}
"""

function sir_ode_jl(u,p,t)
    robj = rcall(:sir_ode_r, u, p, t)
    return convert(Array,robj)
end

Ī“t = 0.1
tmax = 40.0
tspan = (0.0,tmax)
u0 = [990.0,10.0,0.0] # S,I,R
p = [0.05,10.0,0.25] # Ī²,c,Ī³
prob = ODEProblem{false}(sir_ode_jl, u0, tspan, p)
@named sys = modelingtoolkitize(prob)