Bump turing version 0.36

Project.toml

@@ -1,6 +1,6 @@
 name = "SliceSampling"
 uuid = "43f4d3e8-9711-4a8c-bd1b-03ac73a255cf"
-version = "0.7"
+version = "0.8"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -29,7 +29,7 @@ LogDensityProblems = "2"
 LogDensityProblemsAD = "1"
 Random = "1"
 Requires = "1"
-Turing = "0.33, 0.34, 0.35"
+Turing = "0.36"
 julia = "1.10"
 
 [extras]

README.md

@@ -42,7 +42,7 @@ model     = demo()
 sample(model, externalsampler(sampler), n_samples)
 ```
 
-The following slice samplers can also be used as a conditional sampler in `Turing.Experimental.Gibbs` sampler:
+The following slice samplers can also be used as a conditional sampler in `Turing.Gibbs` sampler:
 * For multidimensional variables: 
   * `RandPermGibbs`
   * `HitAndRun`
@@ -69,11 +69,9 @@ using SliceSampling
     end
 end
 
-sampler = Turing.Experimental.Gibbs(
-    (
-        p = externalsampler(SliceSteppingOut(2.0)),
-        z = PG(20, :z)
-    )
+sampler = Turing.Gibbs(
+    p => externalsampler(SliceSteppingOut(2.0)),
+    z => PG(20, :z),
 )
 
 n_samples = 1000

docs/Project.toml

@@ -25,8 +25,8 @@ PDMats = "0.11"
 Plots = "1"
 PrettyTables = "2"
 Random = "1"
-SliceSampling = "0.6, 0.7"
+SliceSampling = "0.8"
 StableRNGs = "1"
 Statistics = "1"
-Turing = "0.34, 0.35"
+Turing = "0.36"
 julia = "1.10"

docs/src/general.md

@@ -60,8 +60,8 @@ model     = demo()
 sample(model, externalsampler(sampler), n_samples)
 ```
 
-### Conditional sampling in a `Turing.Experimental.Gibbs` sampler
-`SliceSampling.jl` be used as a conditional sampler in `Turing.Experimental.Gibbs`.
+### Conditional sampling in a `Turing.Gibbs` sampler
+`SliceSampling.jl` be used as a conditional sampler in `Turing.Gibbs`.
 
 ```@example turinggibbs
 using Distributions
@@ -81,10 +81,8 @@ using SliceSampling
 end
 
 sampler = Turing.Experimental.Gibbs(
-    (
-        p = externalsampler(SliceSteppingOut(2.0)),
-        z = PG(20, :z)
-    )
+    p => externalsampler(SliceSteppingOut(2.0)),
+    z => PG(20, :z),
 )
 
 n_samples = 1000

ext/SliceSamplingTuringExt.jl

@@ -6,13 +6,11 @@ if isdefined(Base, :get_extension)
     using Random
     using SliceSampling
     using Turing
-    # using Turing: Turing, Experimental
 else
     using ..LogDensityProblemsAD
     using ..Random
     using ..SliceSampling
     using ..Turing
-    #using ..Turing: Turing, Experimental
 end
 
 # Required for using the slice samplers as `externalsampler`s in Turing
@@ -24,12 +22,18 @@ function Turing.Inference.getparams(
 end
 # end
 
-# Required for using the slice samplers as `Experimental.Gibbs` samplers in Turing
+# Required for using the slice samplers as `Gibbs` samplers in Turing
 # begin
+Turing.Inference.isgibbscomponent(::SliceSampling.RandPermGibbs) = true
+Turing.Inference.isgibbscomponent(::SliceSampling.HitAndRun) = true
+Turing.Inference.isgibbscomponent(::SliceSampling.Slice) = true
+Turing.Inference.isgibbscomponent(::SliceSampling.SliceSteppingOut) = true
+Turing.Inference.isgibbscomponent(::SliceSampling.SliceDoublingOut) = true
+
 function Turing.Inference.getparams(
-    ::Turing.DynamicPPL.Model, state::SliceSampling.UnivariateSliceState
+    ::Turing.DynamicPPL.Model, sample::SliceSampling.UnivariateSliceState
 )
-    return state.transition.params
+    return sample.transition.params
 end
 
 function Turing.Inference.getparams(
@@ -43,18 +47,6 @@ function Turing.Inference.getparams(
 )
     return state.transition.params
 end
-
-function Turing.Experimental.gibbs_requires_recompute_logprob(
-    model_dst,
-    ::Turing.DynamicPPL.Sampler{
-        <:Turing.Inference.ExternalSampler{<:SliceSampling.AbstractSliceSampling,A,U}
-    },
-    sampler_src,
-    state_dst,
-    state_src,
-) where {A,U}
-    return false
-end
 # end
 
 function SliceSampling.initial_sample(rng::Random.AbstractRNG, ℓ::Turing.LogDensityFunction)

src/multivariate/hitandrun.jl

@@ -17,6 +17,13 @@ struct HitAndRunState{T<:Transition}
     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/randpermgibbs.jl

@@ -25,6 +25,13 @@ struct GibbsState{T<:Transition}
     transition::T
 end
 
+function AbstractMCMC.setparams!!(
+    model::AbstractMCMC.LogDensityModel, state::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

@@ -29,6 +29,13 @@ struct UnivariateSliceState{T<:Transition}
     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

@@ -18,5 +18,5 @@ MCMCTesting = "0.3"
 Random = "1"
 StableRNGs = "1"
 Test = "1"
-Turing = "0.33, 0.34, 0.35"
+Turing = "0.36"
 julia = "1.10"

test/turing.jl

@@ -41,9 +41,7 @@
     ]
         sample(
             model,
-            Turing.Experimental.Gibbs((
-                s=externalsampler(sampler), m=externalsampler(sampler)
-            ),),
+            Turing.Gibbs(:s => externalsampler(sampler), :m => externalsampler(sampler)),
             n_samples;
             progress=false,
         )