Setup formatter (#17)

* apply formatter, add formatting rule
* add Formatter action

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: Red-Portal

.JuliaFormatter.toml

@@ -0,0 +1,7 @@
+
+style = "blue"
+align_assignment = true
+align_struct_field = true
+align_pair_arrow = true
+align_matrix = true
+align_conditional = true

.github/workflows/Format.yml

@@ -0,0 +1,26 @@
+name: Format suggestions
+
+on:
+  pull_request:
+  
+concurrency:
+  # Skip intermediate builds: always.
+  # Cancel intermediate builds: only if it is a pull request build.
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
+
+jobs:
+  format:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: 1
+      - run: |
+          julia  -e 'using Pkg; Pkg.add("JuliaFormatter")'
+          julia  -e 'using JuliaFormatter; format("."; verbose=true)'
+      - uses: reviewdog/action-suggester@v1
+        with:
+          tool_name: JuliaFormatter
+          fail_on_error: true

docs/make.jl

@@ -20,11 +20,8 @@ makedocs(;
         "Univariate Slice Sampling"     => "univariate_slice.md",
         "Meta Multivariate Samplers"    => "meta_multivariate.md",
         "Latent Slice Sampling"         => "latent_slice.md",
-        "Gibbsian Polar Slice Sampling" => "gibbs_polar.md"
+        "Gibbsian Polar Slice Sampling" => "gibbs_polar.md",
     ],
 )
 
-deploydocs(;
-    repo="github.com/TuringLang/SliceSampling.jl",
-    push_preview=true
-)
+deploydocs(; repo="github.com/TuringLang/SliceSampling.jl", push_preview=true)

ext/SliceSamplingTuringExt.jl

@@ -6,7 +6,7 @@ if isdefined(Base, :get_extension)
     using Random
     using SliceSampling
     using Turing
-   # using Turing: Turing, Experimental
+    # using Turing: Turing, Experimental
 else
     using ..LogDensityProblemsAD
     using ..Random
@@ -17,46 +17,47 @@ end
 
 # Required for using the slice samplers as `externalsampler`s in Turing
 # begin
-Turing.Inference.getparams(
-          ::Turing.DynamicPPL.Model,
-    sample::SliceSampling.Transition
-) = sample.params
+function Turing.Inference.getparams(
+    ::Turing.DynamicPPL.Model, sample::SliceSampling.Transition
+)
+    return sample.params
+end
 # end
 
 # Required for using the slice samplers as `Experimental.Gibbs` samplers in Turing
 # begin
-Turing.Inference.getparams(
-         ::Turing.DynamicPPL.Model,
-    state::SliceSampling.UnivariateSliceState
-) = state.transition.params
+function Turing.Inference.getparams(
+    ::Turing.DynamicPPL.Model, state::SliceSampling.UnivariateSliceState
+)
+    return state.transition.params
+end
 
-Turing.Inference.getparams(
-         ::Turing.DynamicPPL.Model,
-    state::SliceSampling.GibbsState
-) = state.transition.params
+function Turing.Inference.getparams(
+    ::Turing.DynamicPPL.Model, state::SliceSampling.GibbsState
+)
+    return state.transition.params
+end
 
-Turing.Inference.getparams(
-         ::Turing.DynamicPPL.Model,
-    state::SliceSampling.HitAndRunState
-) = state.transition.params
+function Turing.Inference.getparams(
+    ::Turing.DynamicPPL.Model, state::SliceSampling.HitAndRunState
+)
+    return state.transition.params
+end
 
-Turing.Experimental.gibbs_requires_recompute_logprob(
+function Turing.Experimental.gibbs_requires_recompute_logprob(
     model_dst,
     ::Turing.DynamicPPL.Sampler{
-        <: Turing.Inference.ExternalSampler{
-            <: SliceSampling.AbstractSliceSampling, A, U
-        }
+        <:Turing.Inference.ExternalSampler{<:SliceSampling.AbstractSliceSampling,A,U}
     },
     sampler_src,
     state_dst,
-    state_src
-) where {A,U} = false
+    state_src,
+) where {A,U}
+    return false
+end
 # end
 
-function SliceSampling.initial_sample(
-    rng::Random.AbstractRNG,
-    ℓ  ::Turing.LogDensityFunction
-)
+function SliceSampling.initial_sample(rng::Random.AbstractRNG, ℓ::Turing.LogDensityFunction)
     model = ℓ.model
     spl   = Turing.SampleFromUniform()
     vi    = Turing.VarInfo(rng, model, spl)
@@ -67,14 +68,14 @@ function SliceSampling.initial_sample(
         if init_attempt_count == 10
             @warn "failed to find valid initial parameters in $(init_attempt_count) tries; consider providing explicit initial parameters using the `initial_params` keyword"
         end
-        
+
         # NOTE: This will sample in the unconstrained space.
         vi = last(DynamicPPL.evaluate!!(model, rng, vi, SampleFromUniform()))
         θ  = vi[spl]
 
         init_attempt_count += 1
     end
-    θ
+    return θ
 end
 
 end

src/SliceSampling.jl

@@ -32,7 +32,7 @@ Struct containing the results of the transition.
 - `lp::Real`: Log-target density of the samples.
 - `info::NamedTuple`: Named tuple containing information about the transition. 
 """
-struct Transition{P, L <: Real, I <: NamedTuple}
+struct Transition{P,L<:Real,I<:NamedTuple}
     "current state of the slice sampling chain"
     params::P
 
@@ -53,47 +53,44 @@ Return the initial sample for the `model` using the random number generator `rng
 - `model`: The target `LogDensityProblem`.
 """
 function initial_sample(::Random.AbstractRNG, ::Any)
-    error(
+    return error(
         "`initial_sample` is not implemented but an initialization wasn't provided. ",
-        "Consider supplying an initialization to `initial_params`."
+        "Consider supplying an initialization to `initial_params`.",
     )
 end
 
 # If target is from `LogDensityProblemsAD`, unwrap target before calling `initial_sample`.
 # This is necessary since Turing wraps `DynamicPPL.Model`s when passed to an `externalsampler`.
-initial_sample(
-    rng::Random.AbstractRNG,
-    wrap::LogDensityProblemsAD.ADGradientWrapper
-) = initial_sample(rng, parent(wrap))
+function initial_sample(
+    rng::Random.AbstractRNG, wrap::LogDensityProblemsAD.ADGradientWrapper
+)
+    return initial_sample(rng, parent(wrap))
+end
 
 function exceeded_max_prop(max_prop::Int)
-    error("Exceeded maximum number of proposal $(max_prop), ",
-          "which indicates an acceptance rate less than $(1/max_prop*100)%. ", 
-          "A quick fix is to increase `max_prop`, ",
-          "but an acceptance rate that is too low often indicates that there is a problem. ",
-          "Here are some possible causes:\n",
-          "- The model might be broken or degenerate (most likely cause).\n",
-          "- The tunable parameters of the sampler are suboptimal.\n",
-          "- The initialization is pathologic. (try supplying a (different) `initial_params`)\n",
-          "- There might be a bug in the sampler. (if this is suspected, file an issue to `SliceSampling`)\n"
-          )
+    return error(
+        "Exceeded maximum number of proposal $(max_prop), ",
+        "which indicates an acceptance rate less than $(1/max_prop*100)%. ",
+        "A quick fix is to increase `max_prop`, ",
+        "but an acceptance rate that is too low often indicates that there is a problem. ",
+        "Here are some possible causes:\n",
+        "- The model might be broken or degenerate (most likely cause).\n",
+        "- The tunable parameters of the sampler are suboptimal.\n",
+        "- The initialization is pathologic. (try supplying a (different) `initial_params`)\n",
+        "- There might be a bug in the sampler. (if this is suspected, file an issue to `SliceSampling`)\n",
+    )
 end
 
 ## Univariate Slice Sampling Algorithms
 export Slice, SliceSteppingOut, SliceDoublingOut
 
-abstract type AbstractUnivariateSliceSampling <: AbstractSliceSampling  end
+abstract type AbstractUnivariateSliceSampling <: AbstractSliceSampling end
 
-accept_slice_proposal(
-    ::AbstractSliceSampling,
-    ::Any,
-    ::Real,
-    ::Real,
-    ::Real,
-    ::Real,
-    ::Real,
-    ::Real,
-) = true
+function accept_slice_proposal(
+    ::AbstractSliceSampling, ::Any, ::Real, ::Real, ::Real, ::Real, ::Real, ::Real
+)
+    return true
+end
 
 function find_interval end
 
@@ -103,7 +100,7 @@ include("univariate/steppingout.jl")
 include("univariate/doublingout.jl")
 
 ## Multivariate slice sampling algorithms
-abstract type AbstractMultivariateSliceSampling <: AbstractSliceSampling  end
+abstract type AbstractMultivariateSliceSampling <: AbstractSliceSampling end
 
 # Meta Multivariate Samplers
 export RandPermGibbs, HitAndRun