Code formatting: JuliaFormatter (#2255)

* Add .JuliaFormatter.toml

* Run JuliaFormatter

* Remove some overly cautious formatter ignores

* Run JuliaFormatter

* Add Format GA workflow

Author: mhauru

.JuliaFormatter.toml

@@ -0,0 +1,25 @@
+style="blue"
+format_markdown = true
+# TODO
+# We ignore these files because when formatting was first put in place they were being worked on.
+# These ignores should be removed once the relevant PRs are merged/closed.
+ignore = [
+    # https://github.com/TuringLang/Turing.jl/pull/2231/files
+    "src/experimental/gibbs.jl",
+    "src/mcmc/abstractmcmc.jl",
+    "test/experimental/gibbs.jl",
+    "test/test_utils/numerical_tests.jl",
+    # https://github.com/TuringLang/Turing.jl/pull/2218/files
+    "src/mcmc/Inference.jl",
+    "test/mcmc/Inference.jl",
+    # https://github.com/TuringLang/Turing.jl/pull/2068
+    "src/variational/VariationalInference.jl",
+    "src/variational/advi.jl",
+    "test/variational/advi.jl",
+    "test/variational/optimisers.jl",
+    # https://github.com/TuringLang/Turing.jl/pull/1887
+    "test/mcmc/Inference.jl",
+    "test/mcmc/hmc.jl",
+    "test/mcmc/sghmc.jl",
+    "test/runtests.jl",
+]

.github/workflows/Format.yml

@@ -0,0 +1,38 @@
+name: Format
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+  merge_group:
+    types: [checks_requested]
+
+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@latest
+        with:
+          version: 1
+      - name: Format code
+        run: |
+          using Pkg
+          Pkg.add(; name="JuliaFormatter", uuid="98e50ef6-434e-11e9-1051-2b60c6c9e899")
+          using JuliaFormatter
+          format("."; verbose=true)
+        shell: julia --color=yes {0}
+      - uses: reviewdog/action-suggester@v1
+        if: github.event_name == 'pull_request'
+        with:
+          tool_name: JuliaFormatter
+          fail_on_error: true

HISTORY.md

@@ -3,16 +3,14 @@
 [![Build Status](https://github.com/TuringLang/Turing.jl/workflows/Turing-CI/badge.svg)](https://github.com/TuringLang/Turing.jl/actions?query=workflow%3ATuring-CI+branch%3Amaster)
 [![Coverage Status](https://coveralls.io/repos/github/TuringLang/Turing.jl/badge.svg?branch=master)](https://coveralls.io/github/TuringLang/Turing.jl?branch=master)
 [![codecov](https://codecov.io/gh/TuringLang/Turing.jl/branch/master/graph/badge.svg?token=OiUBsnDQqf)](https://codecov.io/gh/TuringLang/Turing.jl)
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
-
+[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 
 ## Getting Started
 
 Turing's home page, with links to everything you'll need to use Turing, is available at:
 
 https://turinglang.org/docs/
 
-
 ## What's changed recently?
 
 See [releases](https://github.com/TuringLang/Turing.jl/releases).
@@ -25,6 +23,5 @@ You can see the complete list on Github: https://github.com/TuringLang/Turing.jl
 Turing is an open source project so if you feel you have some relevant skills and are interested in contributing, please get in touch. See the [Contributing](https://turinglang.org/dev/docs/contributing/guide) page for details on the process. You can contribute by opening issues on Github, implementing things yourself, and making a pull request. We would also appreciate example models written using Turing.
 
 ## Issues and Discussions
-Issues related to bugs and feature requests are welcome on the [issues page](https://github.com/TuringLang/Turing.jl/issues), while discussions and questions about statistical applications and theory should place on the [Discussions page](https://github.com/TuringLang/Turing.jl/discussions) or [our channel](https://julialang.slack.com/messages/turing/) (`#turing`) in the Julia Slack chat. If you do not have an invitation to Julia's Slack, you can get one by going [here](https://julialang.org/slack/).
-
 
+Issues related to bugs and feature requests are welcome on the [issues page](https://github.com/TuringLang/Turing.jl/issues), while discussions and questions about statistical applications and theory should place on the [Discussions page](https://github.com/TuringLang/Turing.jl/discussions) or [our channel](https://julialang.slack.com/messages/turing/) (`#turing`) in the Julia Slack chat. If you do not have an invitation to Julia's Slack, you can get one by going [here](https://julialang.org/slack/).

README.md

@@ -16,18 +16,17 @@ BenchmarkSuite["constrained"] = BenchmarkGroup(["constrained"])
 
 data = [0, 1, 0, 1, 1, 1, 1, 1, 1, 1]
 
-
 @model function constrained_test(obs)
-    p ~ Beta(2,2)
-    for i = 1:length(obs)
+    p ~ Beta(2, 2)
+    for i in 1:length(obs)
         obs[i] ~ Bernoulli(p)
     end
-    p
+    return p
 end
 
-
-BenchmarkSuite["constrained"]["constrained"] = @benchmarkable sample($(constrained_test(data)), $(HMC(0.01, 2)), 2000)
-
+BenchmarkSuite["constrained"]["constrained"] = @benchmarkable sample(
+    $(constrained_test(data)), $(HMC(0.01, 2)), 2000
+)
 
 ## gdemo
 
@@ -41,9 +40,9 @@ BenchmarkSuite["gdemo"] = BenchmarkGroup(["gdemo"])
     return s², m
 end
 
-BenchmarkSuite["gdemo"]["hmc"] = @benchmarkable sample($(gdemo(1.5, 2.0)), $(HMC(0.01, 2)), 2000)
-
-
+BenchmarkSuite["gdemo"]["hmc"] = @benchmarkable sample(
+    $(gdemo(1.5, 2.0)), $(HMC(0.01, 2)), 2000
+)
 
 ## MvNormal
 
@@ -52,33 +51,38 @@ BenchmarkSuite["mnormal"] = BenchmarkGroup(["mnormal"])
 # Define the target distribution and its gradient
 
 @model function target(dim)
-   Θ = Vector{Real}(undef, dim)
-   θ ~ MvNormal(zeros(dim), I)
+    Θ = Vector{Real}(undef, dim)
+    return θ ~ MvNormal(zeros(dim), I)
 end
 
 # Sampling parameter settings
 dim = 10
 n_samples = 100_000
 n_adapts = 2_000
 
-BenchmarkSuite["mnormal"]["hmc"] = @benchmarkable sample($(target(dim)), $(HMC(0.1, 5)), $n_samples)
+BenchmarkSuite["mnormal"]["hmc"] = @benchmarkable sample(
+    $(target(dim)), $(HMC(0.1, 5)), $n_samples
+)
 
 ## MvNormal: ForwardDiff vs ReverseDiff
 
 @model function mdemo(d, N)
     Θ = Vector(undef, N)
-   for n=1:N
-      Θ[n] ~ d
-   end
+    for n in 1:N
+        Θ[n] ~ d
+    end
 end
 
 dim2 = 250
-A    = rand(Wishart(dim2, Matrix{Float64}(I, dim2, dim2)));
-d    = MvNormal(zeros(dim2), A)
+A = rand(Wishart(dim2, Matrix{Float64}(I, dim2, dim2)));
+d = MvNormal(zeros(dim2), A)
 
 # ForwardDiff
-BenchmarkSuite["mnormal"]["forwarddiff"] = @benchmarkable sample($(mdemo(d, 1)), $(HMC(0.1, 5; adtype=AutoForwardDiff(; chunksize=0))), 5000)
-
+BenchmarkSuite["mnormal"]["forwarddiff"] = @benchmarkable sample(
+    $(mdemo(d, 1)), $(HMC(0.1, 5; adtype=AutoForwardDiff(; chunksize=0))), 5000
+)
 
 # ReverseDiff
-BenchmarkSuite["mnormal"]["reversediff"] = @benchmarkable sample($(mdemo(d, 1)), $(HMC(0.1, 5; adtype=AutoReverseDiff(false))), 5000)
+BenchmarkSuite["mnormal"]["reversediff"] = @benchmarkable sample(
+    $(mdemo(d, 1)), $(HMC(0.1, 5; adtype=AutoReverseDiff(false))), 5000
+)

benchmarks/benchmarks_suite.jl

@@ -10,20 +10,21 @@ end
 x, y = readlrdata()
 
 @model function hlr_nuts(x, y, θ)
+    N, D = size(x)
 
-    N,D = size(x)
-
-    σ² ~ Exponential(θ)
+    σ² ~ Exponential(θ)
     α ~ Normal(0, sqrt(σ²))
     β ~ MvNormal(zeros(D), σ² * I)
 
-    for n = 1:N
-        y[n] ~ BinomialLogit(1, dot(x[n,:], β) + α)
+    for n in 1:N
+        y[n] ~ BinomialLogit(1, dot(x[n, :], β) + α)
     end
 end
 
 # Sampling parameter settings
 n_samples = 10_000
 
 # Sampling
-BenchmarkSuite["nuts"]["hrl"] = @benchmarkable sample(hlr_nuts(x, y, 1/0.1), NUTS(0.65), n_samples)
+BenchmarkSuite["nuts"]["hrl"] = @benchmarkable sample(
+    hlr_nuts(x, y, 1 / 0.1), NUTS(0.65), n_samples
+)

benchmarks/models/hlr.jl

@@ -10,14 +10,13 @@ end
 X, Y = readlrdata()
 
 @model function lr_nuts(x, y, σ)
-
-    N,D = size(x)
+    N, D = size(x)
 
     α ~ Normal(0, σ)
     β ~ MvNormal(zeros(D), σ^2 * I)
 
-    for n = 1:N
-        y[n] ~ BinomialLogit(1, dot(x[n,:], β) + α)
+    for n in 1:N
+        y[n] ~ BinomialLogit(1, dot(x[n, :], β) + α)
     end
 end
 
@@ -26,5 +25,6 @@ n_samples = 1_000
 n_adapts = 1_000
 
 # Sampling
-BenchmarkSuite["nuts"]["lr"] = @benchmarkable sample(lr_nuts(X, Y, 100),
-                                                     NUTS(0.65), n_samples)
+BenchmarkSuite["nuts"]["lr"] = @benchmarkable sample(
+    lr_nuts(X, Y, 100), NUTS(0.65), n_samples
+)

benchmarks/models/lr.jl

@@ -1,10 +1,9 @@
 using DelimitedFiles
 
 function readlrdata()
-
     fname = joinpath(dirname(@__FILE__), "lr_nuts.data")
     z = readdlm(fname)
-    x = z[:,1:end-1]
-    y = z[:,end] .- 1
+    x = z[:, 1:(end - 1)]
+    y = z[:, end] .- 1
     return x, y
 end

benchmarks/models/lr_helper.jl

@@ -6,26 +6,25 @@ if !haskey(BenchmarkSuite, "nuts")
 end
 
 fname = joinpath(dirname(@__FILE__), "sv_nuts.data")
-y, header = readdlm(fname, ',', header=true)
+y, header = readdlm(fname, ','; header=true)
 
 # Stochastic volatility (SV)
 @model function sv_nuts(y, dy, ::Type{T}=Vector{Float64}) where {T}
-    N = size(y,1)
+    N = size(y, 1)
 
-    τ ~ Exponential(1/100)
-    ν ~ Exponential(1/100)
+    τ ~ Exponential(1 / 100)
+    ν ~ Exponential(1 / 100)
     s = T(undef, N)
 
-    s[1] ~ Exponential(1/100)
+    s[1] ~ Exponential(1 / 100)
     for n in 2:N
-        s[n] ~ Normal(log(s[n-1]), τ)
+        s[n] ~ Normal(log(s[n - 1]), τ)
         s[n] = exp(s[n])
-        dy = log(y[n] / y[n-1]) / s[n]
-        dy ~ TDist(ν)
+        dy = log(y[n] / y[n - 1]) / s[n]
+        dy ~ TDist(ν)
     end
 end
 
-
 # Sampling parameter settings
 n_samples = 10_000
 

benchmarks/models/sv_nuts.jl

@@ -1,5 +1,5 @@
-Turing's documentation in this directory is in markdown format. 
+Turing's documentation in this directory is in markdown format.
 
 If you want to build the doc locally, please refer to the [README](https://github.com/TuringLang/turinglang.github.io) file in [turinglang.github.io](https://github.com/TuringLang/turinglang.github.io).
 
-Please also visit [this repo](https://github.com/TuringLang/TuringTutorials/tree/master/tutorials) for the docs. 
+Please also visit [this repo](https://github.com/TuringLang/TuringTutorials/tree/master/tutorials) for the docs.