Error creating InferenceData from Turing Chains with extra info

[sethaxen]

Currently it seems that if some objects are in the Turing info, we can't map these to the InferenceData info.

using ArviZ, Turing

julia> @model function foo()
    x ~ Normal()
end
foo (generic function with 1 method)

julia> chn = sample(foo(),NUTS(),200); # this is fine

julia> chn.info
NamedTuple()

julia> from_mcmcchains(chn)
InferenceData with groups:
	> posterior
	> sample_stats

julia> chn = sample(foo(),NUTS(),200,;save_state=true) # this will error

julia> chn.info
(model = DynamicPPL.Model{var"#3#4", (), (), (), Tuple{}, Tuple{}}(:foo, var"#3#4"(), NamedTuple(), NamedTuple()), sampler = DynamicPPL.Sampler{NUTS{Turing.Core.ForwardDiffAD{40}, (), AdvancedHMC.DiagEuclideanMetric}}(NUTS{Turing.Core.ForwardDiffAD{40}, (), AdvancedHMC.DiagEuclideanMetric}(-1, 0.65, 10, 1000.0, 0.0), DynamicPPL.Selector(0x00016a8da36513f2, :default, false)), samplerstate = Turing.Inference.HMCState{DynamicPPL.TypedVarInfo{NamedTuple{(:x,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Tuple{}}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Tuple{}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, AdvancedHMC.NUTS{AdvancedHMC.MultinomialTS, AdvancedHMC.GeneralisedNoUTurn, AdvancedHMC.Leapfrog{Float64}, Float64}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, Turing.Inference.var"#logπ#54"{DynamicPPL.TypedVarInfo{NamedTuple{(:x,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Tuple{}}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Tuple{}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Sampler{NUTS{Turing.Core.ForwardDiffAD{40}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.Model{var"#3#4", (), (), (), Tuple{}, Tuple{}}}, Turing.Inference.var"#∂logπ∂θ#53"{DynamicPPL.TypedVarInfo{NamedTuple{(:x,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Tuple{}}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Tuple{}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Sampler{NUTS{Turing.Core.ForwardDiffAD{40}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.Model{var"#3#4", (), (), (), Tuple{}, Tuple{}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64}}}(DynamicPPL.TypedVarInfo{NamedTuple{(:x,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Tuple{}}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Tuple{}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}((x = DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Tuple{}}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Tuple{}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}(Dict(x => 1), [x], UnitRange{Int64}[1:1], [0.007224315165188178], Normal{Float64}[Normal{Float64}(μ=0.0, σ=1.0)], Set{DynamicPPL.Selector}[Set([DynamicPPL.Selector(0x00016a8da36513f2, :default, false)])], [0], Dict{String, BitVector}("del" => [0], "trans" => [1])),), Base.RefValue{Float64}(-0.9189646285694758), Base.RefValue{Int64}(0)), 299, NUTS{MultinomialTS,Generalised}(integrator=Leapfrog(ϵ=1.43), max_depth=10), Δ_max=1000.0), Hamiltonian(metric=DiagEuclideanMetric([1.0])), AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}([0.007224315165188178], [-0.5308342150394731], AdvancedHMC.DualValue{Float64, Vector{Float64}}(-0.9189646285694758, [0.007224315165188178]), AdvancedHMC.DualValue{Float64, Vector{Float64}}(-0.14089248192828682, [-0.5308342150394731])), StanHMCAdaptor(
    pc=WelfordVar,
    ssa=NesterovDualAveraging(γ=0.05, t_0=10.0, κ=0.75, δ=0.65, state.ϵ=1.425166901462951),
    init_buffer=75, term_buffer=50, window_size=25,
    state=window(76, 50), window_splits()
)))

julia> from_mcmcchains(chn)
ERROR: PyError ($(Expr(:escape, :(ccall(#= /Users/sethaxen/.julia/packages/PyCall/L0fLP/src/pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) <class 'TypeError'>
TypeError("cannot pickle 'PyCall.jlwrap' object")
  File "/Users/sethaxen/.julia/conda/3/lib/python3.8/site-packages/arviz/data/inference_data.py", line 1837, in concat
    args_groups[group] = deepcopy(group_data) if copy else group_data
  File "/Users/sethaxen/.julia/conda/3/lib/python3.8/copy.py", line 153, in deepcopy
    y = copier(memo)
  File "/Users/sethaxen/.julia/conda/3/lib/python3.8/site-packages/xarray/core/dataset.py", line 1425, in __deepcopy__
    return self.copy(deep=True)
  File "/Users/sethaxen/.julia/conda/3/lib/python3.8/site-packages/xarray/core/dataset.py", line 1322, in copy
    attrs = copy.deepcopy(self._attrs) if deep else copy.copy(self._attrs)
  File "/Users/sethaxen/.julia/conda/3/lib/python3.8/copy.py", line 146, in deepcopy
    y = copier(x, memo)
  File "/Users/sethaxen/.julia/conda/3/lib/python3.8/copy.py", line 230, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/Users/sethaxen/.julia/conda/3/lib/python3.8/copy.py", line 161, in deepcopy
    rv = reductor(4)

We should probably filter the info on our end before InferenceData creation so that these errors can't happen.

[sethaxen]

On recent versions this no longer errors. Instead, it seems the info is just excluded, which isn't what I think we want to do.