Update for newer AbstractMCMC/Turing interface

.gitignore

@@ -4,5 +4,5 @@
 /docs/Manifest.toml
 /docs/build/
 **/*~
-Maniest.toml
-test/Manifest.toml
+Manifest.toml
+test/Manifest.toml

Project.toml

@@ -1,32 +1,20 @@
 name = "SliceSampling"
 uuid = "43f4d3e8-9711-4a8c-bd1b-03ac73a255cf"
-version = "0.7.10"
+version = "0.7.11"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
+Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
-[weakdeps]
-Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-
-[extensions]
-SliceSamplingTuringExt = ["Turing"]
-
 [compat]
-AbstractMCMC = "4, 5"
+AbstractMCMC = "5.9"
+Accessors = "0.1"
 Distributions = "0.25"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
 Random = "1"
-Turing = "0.41"
 julia = "1.10"
-
-[extras]
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-
-[targets]
-test = ["Test"]

docs/Project.toml

@@ -15,7 +15,7 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
-AbstractMCMC = "5"
+AbstractMCMC = "5.9"
 Distributions = "0.25"
 Documenter = "1"
 FillArrays = "1"
@@ -28,5 +28,5 @@ Random = "1"
 SliceSampling = "0.7.1"
 StableRNGs = "1"
 Statistics = "1"
-Turing = "0.41, 0.42"
+Turing = "0.42"
 julia = "1.10"

docs/src/general.md

@@ -101,13 +101,13 @@ sample([rng,] model, slice, N; initial_params)
 - `model`: A model implementing the `LogDensityProblems` interface.
 - `N`: The number of samples
 
-The output is a `SliceSampling.Transition` object, which contains the following:
+The output is a vector of `SliceSampling.Transition`s, which contains the following:
 ```@docs
 SliceSampling.Transition
 ```
 
 For the keyword arguments, `SliceSampling` allows:
-- `initial_params`: The intial state of the Markov chain (default: `nothing`).
+- `initial_params`: The initial state of the Markov chain (default: `nothing`).
 
 If `initial_params` is `nothing`, the following function can be implemented to provide an initialization:
 ```@docs

src/SliceSampling.jl

@@ -2,6 +2,7 @@
 module SliceSampling
 
 using AbstractMCMC
+using Accessors: Accessors
 using Distributions
 using LinearAlgebra
 using LogDensityProblems
@@ -37,6 +38,18 @@ struct Transition{P,L<:Real,I<:NamedTuple}
     info::I
 end
 
+# Base type for MCMC states that contain a `Transition` stored in the `transition` field.
+abstract type AbstractStateWithTransition end
+AbstractMCMC.getparams(state::AbstractStateWithTransition) = state.transition.params
+AbstractMCMC.getstats(state::AbstractStateWithTransition) = state.transition.info
+function AbstractMCMC.setparams!!(
+    model::AbstractMCMC.LogDensityModel, state::AbstractStateWithTransition, params
+)
+    new_lp = LogDensityProblems.logdensity(model.logdensity, params)
+    new_transition = Transition(params, new_lp, NamedTuple())
+    return Accessors.@set state.transition = new_transition
+end
+
 """
     initial_sample(rng, model)
 

src/multivariate/gibbspolar.jl

@@ -29,7 +29,8 @@ function GibbsPolarSlice(w::Real; max_proposals::Int=DEFAULT_MAX_PROPOSALS)
     return GibbsPolarSlice(w, max_proposals)
 end
 
-struct GibbsPolarSliceState{T<:Transition,R<:Real,D<:AbstractVector}
+struct GibbsPolarSliceState{T<:Transition,R<:Real,D<:AbstractVector} <:
+       AbstractStateWithTransition
     "Current [`Transition`](@ref)."
     transition::T
 

src/multivariate/hitandrun.jl

@@ -12,18 +12,11 @@ struct HitAndRun{S<:AbstractUnivariateSliceSampling} <: AbstractMultivariateSlic
     unislice::S
 end
 
-struct HitAndRunState{T<:Transition}
+struct HitAndRunState{T<:Transition} <: AbstractStateWithTransition
     "Current [`Transition`](@ref)."
     transition::T
 end
 
-function AbstractMCMC.setparams!!(
-    model::AbstractMCMC.LogDensityModel, state::HitAndRunState, params
-)
-    lp = LogDensityProblems.logdensity(model.logdensity, params)
-    return HitAndRunState(Transition(params, lp, NamedTuple()))
-end
-
 struct HitAndRunTarget{Model,Vec<:AbstractVector}
     model     :: Model
     direction :: Vec

src/multivariate/latent.jl

@@ -20,7 +20,7 @@ function LatentSlice(beta::Real; max_proposals::Int=DEFAULT_MAX_PROPOSALS)
     return LatentSlice(beta, max_proposals)
 end
 
-struct LatentSliceState{T<:Transition,S<:AbstractVector}
+struct LatentSliceState{T<:Transition,S<:AbstractVector} <: AbstractStateWithTransition
     "Current [`Transition`](@ref)."
     transition::T
 

src/multivariate/randpermgibbs.jl

@@ -20,16 +20,11 @@ struct RandPermGibbs{
     unislice::S
 end
 
-struct GibbsState{T<:Transition}
+struct GibbsState{T<:Transition} <: AbstractStateWithTransition
     "Current [`Transition`](@ref)."
     transition::T
 end
 
-function AbstractMCMC.setparams!!(model::AbstractMCMC.LogDensityModel, ::GibbsState, params)
-    lp = LogDensityProblems.logdensity(model.logdensity, params)
-    return GibbsState(Transition(params, lp, NamedTuple()))
-end
-
 struct GibbsTarget{Model,Idx<:Integer,Vec<:AbstractVector}
     model :: Model
     idx   :: Idx

src/univariate/univariate.jl

@@ -24,18 +24,11 @@ function slice_sampling_univariate(
     return exceeded_max_prop(max_prop)
 end
 
-struct UnivariateSliceState{T<:Transition}
+struct UnivariateSliceState{T<:Transition} <: AbstractStateWithTransition
     "Current [`Transition`](@ref)."
     transition::T
 end
 
-function AbstractMCMC.setparams!!(
-    model::AbstractMCMC.LogDensityModel, state::UnivariateSliceState, params
-)
-    lp = LogDensityProblems.logdensity(model.logdensity, params)
-    return UnivariateSliceState(Transition(params, lp, NamedTuple()))
-end
-
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
     model::AbstractMCMC.LogDensityModel,

test/Project.toml

@@ -2,6 +2,7 @@
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 MCMCTesting = "9963b6a1-5d46-439c-8efc-3a487843c7fa"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -10,13 +11,14 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
-AbstractMCMC = "4, 5"
+AbstractMCMC = "5.9"
 Accessors = "0.1"
 Distributions = "0.25"
+DynamicPPL = "0.39.12"
 LogDensityProblems = "2"
 MCMCTesting = "0.3"
 Random = "1"
 StableRNGs = "1"
 Test = "1"
-Turing = "0.41, 0.42"
+Turing = "0.42"
 julia = "1.10"

test/turing.jl

@@ -8,30 +8,11 @@
         return nothing
     end
 
-    @model function illbehavedmodel()
-        @addlogprob! -Inf
-        return nothing
-    end
-
     @model function logp_check()
         a ~ Normal()
         return b ~ Normal()
     end
 
-    rng = Random.default_rng()
-    @test begin
-        init = SliceSampling.initial_sample(rng, LogDensityFunction(demo()))
-        all(isfinite.(init))
-    end
-
-    @test_warn "Warning: Failed" SliceSampling.initial_sample(
-        rng, LogDensityFunction(illbehavedmodel())
-    )
-
-    @test_warn "Error: Failed" SliceSampling.initial_sample(
-        rng, LogDensityFunction(illbehavedmodel())
-    )
-
     n_samples = 1000
     model = demo()
 
@@ -61,7 +42,7 @@
         @test isapprox(
             logpdf.(Normal(), chain_logp_check[:a]) .+
             logpdf.(Normal(), chain_logp_check[:b]),
-            chain_logp_check[:lp],
+            chain_logp_check[:logjoint],
         )
     end
 
@@ -89,7 +70,7 @@
         @test isapprox(
             logpdf.(Normal(), chain_logp_check[:a]) .+
             logpdf.(Normal(), chain_logp_check[:b]),
-            chain_logp_check[:lp],
+            chain_logp_check[:logjoint],
         )
     end
 end