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torfjelde/stan-within-turing.jl

Last active September 2, 2023 11:31
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Rough example of using StanDistributions.jl within Turing.jl.
Raw
stan-within-turing.jl
julia> using PosteriorDB, StanDistributions, Turing, BridgeStan, LinearAlgebra
julia> # Necessary overloads to make it work with Turing.
function DynamicPPL.init(rng, dist::StanDistribution, ::DynamicPPL.SampleFromPrior)
# `init` uses `rand` by default but this is not supported for `StanDistribution`.
return BridgeStan.param_constrain(dist.model, randn(rng, length(dist)))
end
julia> function DynamicPPL.with_logabsdet_jacobian_and_reconstruct(f, dist::StanDistribution, x)
# HACK: This is cheating.
return (f(x), zero(eltype(x)))
end
julia> function Bijectors.logpdf_with_trans(dist::StanDistribution, x::AbstractVector, istrans::Bool)
istrans || return logpdf(dist, x)
# Because `x` is always constrained. Yeah I know, I hate it too.
y = Bijectors.transform(bijector(dist), x)
return logpdf(StanDistributions._unconstrain(dist), y)
end
julia> # Set up Stan distribution.
pdb = PosteriorDB.database();
julia> post = PosteriorDB.posterior(pdb, "eight_schools-eight_schools_noncentered");
julia> stan_file = cp(PosteriorDB.path(PosteriorDB.implementation(PosteriorDB.model(post), "stan")), "stan_file.stan", force=true)
"stan_file.stan"
julia> stan_data = PosteriorDB.load(PosteriorDB.dataset(post), String)
"{\n \"J\": 8,\n \"y\": [28, 8, -3, 7, -1, 1, 18, 12],\n \"sigma\": [15, 10, 16, 11, 9, 11, 10, 18]\n}\n"
julia> dist = StanDistribution(stan_file, stan_data);
┌ Warning: Loading a shared object '/drive-2/Projects/public/DynamicPPL.jl/stan_file_model.so' which is already loaded.
│ If the file has changed since the last time it was loaded, this load may not update the library!
└ @ BridgeStan ~/.julia/packages/BridgeStan/r4RPQ/src/model.jl:51
julia> # Create model.
@model demo() = x ~ dist
demo (generic function with 2 methods)
julia> model = demo()
DynamicPPL.Model{typeof(demo), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}(demo, NamedTuple(), NamedTuple(), DynamicPPL.DefaultContext())
julia> # Sample.
chain = sample(model, MH(Diagonal(ones(length(dist)))), 1000)
Sampling 100%|██████████████████████████████████████████████████████████████████████████████████████| Time: 0:00:00
Chains MCMC chain (1000×11×1 Array{Float64, 3}):
Iterations = 1:1:1000
Number of chains = 1
Samples per chain = 1000
Wall duration = 0.28 seconds
Compute duration = 0.28 seconds
parameters = x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10]
internals = lp
Summary Statistics
parameters mean std mcse ess_bulk ess_tail rhat ess_per_sec
Symbol Float64 Float64 Float64 Float64 Float64 Float64 Float64
x[1] 0.3018 0.8761 0.2046 19.6808 54.0652 1.1257 69.2986
x[2] -0.0315 0.7574 0.1268 33.8723 38.8298 1.0469 119.2686
x[3] -0.4987 0.9294 0.1768 29.4710 31.3870 1.0086 103.7712
x[4] -0.1405 0.7941 0.1713 20.0057 54.8830 1.0954 70.4428
x[5] -0.3183 0.6575 0.0991 45.9851 56.2364 1.0115 161.9194
x[6] 0.0989 0.8708 0.1710 25.7288 33.8405 1.0148 90.5943
x[7] 0.6967 0.9422 0.1373 46.4741 62.0100 1.0055 163.6412
x[8] 0.1594 0.8737 0.1508 33.1242 79.3680 1.0323 116.6344
x[9] 4.1155 2.1446 0.9550 5.3140 24.7503 1.1598 18.7112
x[10] 4.3723 3.0414 0.5701 17.9901 45.3636 1.0880 63.3455
Quantiles
parameters 2.5% 25.0% 50.0% 75.0% 97.5%
Symbol Float64 Float64 Float64 Float64 Float64
x[1] -1.5304 -0.2382 0.3018 0.9397 1.9190
x[2] -1.6546 -0.4930 -0.0070 0.4749 1.2945
x[3] -1.9595 -1.0840 -0.5131 -0.0810 1.5832
x[4] -2.1416 -0.5176 -0.1250 0.3843 1.1761
x[5] -1.3227 -0.8960 -0.3740 0.1333 1.0498
x[6] -1.2090 -0.5686 0.0243 0.8413 1.7140
x[7] -1.2315 0.2178 0.6879 1.3352 2.4447
x[8] -1.3547 -0.5421 0.3318 0.7988 1.6284
x[9] 0.2154 2.5220 4.2424 5.8616 7.9825
x[10] 0.4093 2.2244 3.8886 5.9015 12.4081
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Note that this only works with (some) non-AD samplers, as we AD-based samplers require differentiation through Stan's transformation from unconstrained to constrained.

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uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.UnsafeAtomics]]
git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278"
uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f"
version = "0.2.1"

[[deps.UnsafeAtomicsLLVM]]
deps = ["LLVM", "UnsafeAtomics"]
git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e"
uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249"
version = "0.1.3"

[[deps.WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.5"

[[deps.ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.10.1"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+0"

[[deps.ZygoteRules]]
deps = ["ChainRulesCore", "MacroTools"]
git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.3"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.8.0+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.48.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"

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