fix apply custom formatter

Author: Red-Portal

docs/make.jl

@@ -1,20 +1,20 @@
 using SliceSampling
 using Documenter
 
-DocMeta.setdocmeta!(SliceSampling, :DocTestSetup, :(using SliceSampling); recursive = true)
+DocMeta.setdocmeta!(SliceSampling, :DocTestSetup, :(using SliceSampling); recursive=true)
 
 makedocs(;
-    modules = [SliceSampling],
-    authors = "Kyurae Kim <[email protected]> and contributors",
-    repo = "https://github.com/TuringLang/SliceSampling.jl/blob/{commit}{path}#{line}",
-    sitename = "SliceSampling.jl",
-    format = Documenter.HTML(;
-        prettyurls = get(ENV, "CI", "false") == "true",
-        canonical = "https://TuringLang.org/SliceSampling.jl",
-        edit_link = "main",
-        assets = String[],
+    modules=[SliceSampling],
+    authors="Kyurae Kim <[email protected]> and contributors",
+    repo="https://github.com/TuringLang/SliceSampling.jl/blob/{commit}{path}#{line}",
+    sitename="SliceSampling.jl",
+    format=Documenter.HTML(;
+        prettyurls=get(ENV, "CI", "false") == "true",
+        canonical="https://TuringLang.org/SliceSampling.jl",
+        edit_link="main",
+        assets=String[],
     ),
-    pages = [
+    pages=[
         "Home" => "index.md",
         "General Usage" => "general.md",
         "Univariate Slice Sampling" => "univariate_slice.md",
@@ -24,4 +24,4 @@ makedocs(;
     ],
 )
 
-deploydocs(; repo = "github.com/TuringLang/SliceSampling.jl", push_preview = true)
+deploydocs(; repo="github.com/TuringLang/SliceSampling.jl", push_preview=true)

ext/SliceSamplingTuringExt.jl

@@ -17,32 +17,44 @@ 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
+) where {A,U}
+    return false
+end
 # end
 
 function SliceSampling.initial_sample(rng::Random.AbstractRNG, ℓ::Turing.LogDensityFunction)
@@ -63,7 +75,7 @@ function SliceSampling.initial_sample(rng::Random.AbstractRNG, ℓ::Turing.LogDe
 
         init_attempt_count += 1
     end
-    θ
+    return θ
 end
 
 end

src/SliceSampling.jl

@@ -53,19 +53,22 @@ 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`.",
     )
 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(
+    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`, ",
@@ -83,16 +86,11 @@ export Slice, SliceSteppingOut, SliceDoublingOut
 
 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
 
@@ -127,7 +125,7 @@ end
 @static if !isdefined(Base, :get_extension)
     function __init__()
         @require Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" include(
-            "../ext/SliceSamplingTuringExt.jl",
+            "../ext/SliceSamplingTuringExt.jl"
         )
     end
 end

src/multivariate/gibbspolar.jl

@@ -28,8 +28,9 @@ struct GibbsPolarSlice{W<:Real} <: AbstractMultivariateSliceSampling
     max_proposals::Int
 end
 
-GibbsPolarSlice(w::Real; max_proposals::Int = DEFAULT_MAX_PROPOSALS) =
-    GibbsPolarSlice(w, max_proposals)
+function GibbsPolarSlice(w::Real; max_proposals::Int=DEFAULT_MAX_PROPOSALS)
+    return GibbsPolarSlice(w, max_proposals)
+end
 
 struct GibbsPolarSliceState{T<:Transition,R<:Real,D<:AbstractVector}
     "Current [`Transition`](@ref)."
@@ -48,14 +49,14 @@ end
 
 function logdensity(target::GibbsPolarSliceTarget, x)
     d = length(x)
-    (d - 1) * log(norm(x)) + LogDensityProblems.logdensity(target.model, x)
+    return (d - 1) * log(norm(x)) + LogDensityProblems.logdensity(target.model, x)
 end
 
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
     model::AbstractMCMC.LogDensityModel,
     sampler::GibbsPolarSlice;
-    initial_params = nothing,
+    initial_params=nothing,
     kwargs...,
 )
     logdensitymodel = model.logdensity
@@ -76,7 +77,7 @@ function rand_subsphere(rng::Random.AbstractRNG, θ::AbstractVector)
     d = length(θ)
     V1 = randn(rng, eltype(θ), d)
     V2 = V1 - dot(θ, V1) * θ
-    V2 / max(norm(V2), eps(eltype(θ)))
+    return V2 / max(norm(V2), eps(eltype(θ)))
 end
 
 function geodesic_shrinkage(
@@ -91,7 +92,7 @@ function geodesic_shrinkage(
     ω_max = convert(F, 2π) * rand(rng, F)
     ω_min = ω_max - convert(F, 2π)
 
-    for n_props = 1:max_prop
+    for n_props in 1:max_prop
         # `Uniform` had a type instability issue:
         # https://github.com/JuliaStats/Distributions.jl/pull/1860
         # ω = rand(rng, Uniform(ω_min, ω_max))
@@ -108,7 +109,7 @@ function geodesic_shrinkage(
             ω_max = ω
         end
     end
-    exceeded_max_prop(max_prop)
+    return exceeded_max_prop(max_prop)
 end
 
 function radius_shrinkage(
@@ -148,7 +149,7 @@ function radius_shrinkage(
     end
     n_props_total += n_props
 
-    for n_props = 1:max_prop
+    for n_props in 1:max_prop
         # `Uniform` had a type instability issue:
         # https://github.com/JuliaStats/Distributions.jl/pull/1860
         #r′ = rand(rng, Uniform{F}(r_min, r_max))
@@ -165,7 +166,7 @@ function radius_shrinkage(
             r_max = r′
         end
     end
-    exceeded_max_prop(max_prop)
+    return exceeded_max_prop(max_prop)
 end
 
 function AbstractMCMC.step(
@@ -194,9 +195,7 @@ function AbstractMCMC.step(
 
     ℓp = LogDensityProblems.logdensity(logdensitymodel, x)
     t = Transition(
-        x,
-        ℓp,
-        (num_radius_proposals = n_props_r, num_direction_proposals = n_props_θ),
+        x, ℓp, (num_radius_proposals=n_props_r, num_direction_proposals=n_props_θ)
     )
-    t, GibbsPolarSliceState(t, θ, r)
+    return t, GibbsPolarSliceState(t, θ, r)
 end

src/multivariate/hitandrun.jl

@@ -25,14 +25,14 @@ end
 
 function LogDensityProblems.logdensity(target::HitAndRunTarget, λ)
     (; model, reference, direction) = target
-    LogDensityProblems.logdensity(model, reference + λ * direction)
+    return LogDensityProblems.logdensity(model, reference + λ * direction)
 end
 
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
     model::AbstractMCMC.LogDensityModel,
     sampler::HitAndRun;
-    initial_params = nothing,
+    initial_params=nothing,
     kwargs...,
 )
     logdensitymodel = model.logdensity
@@ -46,7 +46,7 @@ end
 
 function rand_uniform_unit_sphere(rng::Random.AbstractRNG, type::Type, d::Int)
     x = randn(rng, type, d)
-    x / norm(x)
+    return x / norm(x)
 end
 
 function AbstractMCMC.step(
@@ -67,6 +67,6 @@ function AbstractMCMC.step(
     λ = zero(eltype(θ))
     λ, ℓp, props = slice_sampling_univariate(rng, unislice, hnrtarget, ℓp, λ)
     θ′ = θ + direction * λ
-    t = Transition(θ′, ℓp, (num_proposals = props,))
-    t, HitAndRunState(t)
+    t = Transition(θ′, ℓp, (num_proposals=props,))
+    return t, HitAndRunState(t)
 end

src/multivariate/latent.jl

@@ -15,9 +15,9 @@ struct LatentSlice{B<:Real} <: AbstractMultivariateSliceSampling
     max_proposals::Int
 end
 
-function LatentSlice(beta::Real; max_proposals::Int = DEFAULT_MAX_PROPOSALS)
+function LatentSlice(beta::Real; max_proposals::Int=DEFAULT_MAX_PROPOSALS)
     @assert beta > 0 "Beta must be strictly positive"
-    LatentSlice(beta, max_proposals)
+    return LatentSlice(beta, max_proposals)
 end
 
 struct LatentSliceState{T<:Transition,S<:AbstractVector}
@@ -32,7 +32,7 @@ function AbstractMCMC.step(
     rng::Random.AbstractRNG,
     model::AbstractMCMC.LogDensityModel,
     sampler::LatentSlice;
-    initial_params = nothing,
+    initial_params=nothing,
     kwargs...,
 )
     logdensitymodel = model.logdensity
@@ -85,7 +85,7 @@ function AbstractMCMC.step(
             exceeded_max_prop(max_proposals)
         end
 
-        @inbounds for i = 1:d
+        @inbounds for i in 1:d
             if ystar[i] < y[i]
                 a[i] = ystar[i]
             else
@@ -94,6 +94,6 @@ function AbstractMCMC.step(
         end
     end
     s = β * randexp(rng, eltype(y), d) + 2 * abs.(l - y)
-    t = Transition(y, ℓp, (num_proposals = props,))
-    t, LatentSliceState(t, s)
+    t = Transition(y, ℓp, (num_proposals=props,))
+    return t, LatentSliceState(t, s)
 end

src/multivariate/randpermgibbs.jl

@@ -33,14 +33,14 @@ end
 
 function LogDensityProblems.logdensity(gibbs::GibbsTarget, θi)
     (; model, idx, θ) = gibbs
-    LogDensityProblems.logdensity(model, (@set θ[idx] = θi))
+    return LogDensityProblems.logdensity(model, (@set θ[idx] = θi))
 end
 
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
     model::AbstractMCMC.LogDensityModel,
     sampler::RandPermGibbs;
-    initial_params = nothing,
+    initial_params=nothing,
     kwargs...,
 )
     logdensitymodel = model.logdensity
@@ -75,11 +75,12 @@ function AbstractMCMC.step(
     for i in shuffle(rng, 1:d)
         model_gibbs = GibbsTarget(logdensitymodel, i, θ)
         unislice = unislices[i]
-        θ′_coord, ℓp, props_coord =
-            slice_sampling_univariate(rng, unislice, model_gibbs, ℓp, θ[i])
+        θ′_coord, ℓp, props_coord = slice_sampling_univariate(
+            rng, unislice, model_gibbs, ℓp, θ[i]
+        )
         props[i] = props_coord
         θ[i] = θ′_coord
     end
-    t = Transition(θ, ℓp, (num_proposals = props,))
-    t, GibbsState(t)
+    t = Transition(θ, ℓp, (num_proposals=props,))
+    return t, GibbsState(t)
 end