Automatic differentiation through DataFrame or any other DataFrame-like tables

New to Julia
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1

Hi, I’m inspecting some functionality of DiffEqFlux.jl by modifying DiffEqFlux.jl’s examples.

I wonder if it’s possible to save trajectory data as DataFrame and take automatic differentiation (via e.g. Zygote) through the DataFrame.
Is it possible?

I tried some, but I failed.

1 Like
2

I leave some observations.

  • AD works
  1. inserting trajectory data a new array works, e.g., output = [dummy, trajectory_data]
  2. inserting trajectory data a new NamedTuple works, e.g., output = (; dummy=dummy, u=trajectory_data)
  • AD not works
  1. inserting trajectory data a new Dict works, e.g., output = Dict(:dummy => dummy, :u => trajectory_data)
    • Error message
      ERROR: Compiling Tuple{Type{Dict}, Tuple{Pair{String, Vector{Float64}}, Pair{String, Vector{Vector{Float64}}}}}: try/catch is not supported.
Stacktrace:
[1] error(s::String)
  @ Base ./error.jl:33
[2] instrument(ir::IRTools.Inner.IR)
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/reverse.jl:121
[3] #Primal#20
  @ ~/.julia/packages/Zygote/bJn8I/src/compiler/reverse.jl:202 [inlined]
[4] Zygote.Adjoint(ir::IRTools.Inner.IR; varargs::Nothing, normalise::Bool)
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/reverse.jl:315
[5] _generate_pullback_via_decomposition(T::Type)
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/emit.jl:101
[6] #s3063#1218
  @ ~/.julia/packages/Zygote/bJn8I/src/compiler/interface2.jl:28 [inlined]
[7] var"#s3063#1218"(::Any, ctx::Any, f::Any, args::Any)
  @ Zygote ./none:0
[8] (::Core.GeneratedFunctionStub)(::Any, ::Vararg{Any})
  @ Core ./boot.jl:580
[9] _pullback
  @ ./dict.jl:125 [inlined]
[10] _pullback(::Zygote.Context, ::Type{Dict}, ::Pair{String, Vector{Float64}}, ::Pair{String, Vector{Vector{Float64}}})
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/interface2.jl:0
[11] _pullback
  @ ~/.julia/dev/ContinuousTimePolicyGradients/test/model-estimation/toy.jl:44 [inlined]
[12] _pullback(::Zygote.Context, ::var"#predict_n_ode#168"{Vector{Float32}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Vector{Float64}})
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/interface2.jl:0
[13] _pullback
  @ ~/.julia/dev/ContinuousTimePolicyGradients/test/model-estimation/toy.jl:52 [inlined]
[14] _pullback(::Zygote.Context, ::var"#loss_n_ode#169"{var"#predict_n_ode#168"{Vector{Float32}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Vector{Float64}}, Dict{String, Vector}})
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/interface2.jl:0
[15] _apply
  @ ./boot.jl:814 [inlined]
[16] adjoint
  @ ~/.julia/packages/Zygote/bJn8I/src/lib/lib.jl:200 [inlined]
[17] _pullback
  @ ~/.julia/packages/ZygoteRules/AIbCs/src/adjoint.jl:65 [inlined]
[18] _pullback
  @ ~/.julia/packages/Flux/BPPNj/src/optimise/train.jl:105 [inlined]
[19] _pullback(::Zygote.Context, ::Flux.Optimise.var"#39#45"{var"#loss_n_ode#169"{var"#predict_n_ode#168"{Vector{Float32}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Vector{Float64}}, Dict{String, Vector}}, Tuple{}})
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/interface2.jl:0
[20] pullback(f::Function, ps::Params)
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/interface.jl:351
[21] gradient(f::Function, args::Params)
  @ Zygote ~/.julia/packages/Zygote/bJn8I/src/compiler/interface.jl:75
[22] macro expansion
  @ ~/.julia/packages/Flux/BPPNj/src/optimise/train.jl:104 [inlined]
[23] macro expansion
  @ ~/.julia/packages/Juno/n6wyj/src/progress.jl:134 [inlined]
[24] train!(loss::Function, ps::Params, data::Base.Iterators.Take{Base.Iterators.Repeated{Tuple{}}}, opt::ADAM; cb::var"#164#170"{var"#164#165#171"{var"#predict_n_ode#168"{Vector{Float32}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Vector{Float64}}, Dict{String, Vector}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}}})
  @ Flux.Optimise ~/.julia/packages/Flux/BPPNj/src/optimise/train.jl:102
[25] main()
  @ Main ~/.julia/dev/ContinuousTimePolicyGradients/test/model-estimation/toy.jl:74
[26] top-level scope
  @ REPL[25]:1
[27] top-level scope
  @ ~/.julia/packages/CUDA/YpW0k/src/initialization.jl:52