Fix deprecations and reduce CI tests (#21)

* Fix deprecations

* Reduce CI

* Fix CI

* Update CompatHelper

.github/workflows/CI.yml

@@ -19,19 +19,18 @@ jobs:
           - 'nightly'
         os:
           - ubuntu-latest
-          - macOS-latest 
-          - windows-latest
         arch:
-          - x86
           - x64
-        exclude:
-          - os: macOS-latest
-            arch: x86
         include:
           - version: '1'
             os: ubuntu-latest
+            arch: x86
+          - version: '1'
+            os: macOS-latest
+            arch: x64
+          - version: '1'
+            os: windows-latest
             arch: x64
-            coverage: true
     steps:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@v1
@@ -50,16 +49,18 @@ jobs:
             ${{ runner.os }}-
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
+        with:
+          coverage: ${{ matrix.version == '1' && matrix.os == 'ubuntu-latest' && matrix.arch == 'x64' }}
         env:
           JULIA_NUM_THREADS: 2
       - uses: julia-actions/julia-processcoverage@v1
-        if: matrix.coverage
+        if: matrix.version == '1' && matrix.os == 'ubuntu-latest' && matrix.arch == 'x64'
       - uses: codecov/codecov-action@v1
-        if: matrix.coverage
+        if: matrix.version == '1' && matrix.os == 'ubuntu-latest' && matrix.arch == 'x64'
         with:
           file: lcov.info
       - uses: coverallsapp/github-action@master
-        if: matrix.coverage
+        if: matrix.version == '1' && matrix.os == 'ubuntu-latest' && matrix.arch == 'x64'
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           path-to-lcov: lcov.info

.github/workflows/CompatHelper.yml

@@ -1,16 +1,31 @@
 name: CompatHelper
 on:
   schedule:
-    - cron: '00 00 * * *'
+    - cron: 0 0 * * *
   workflow_dispatch:
 jobs:
   CompatHelper:
     runs-on: ubuntu-latest
     steps:
-      - name: Pkg.add("CompatHelper")
-        run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
-      - name: CompatHelper.main()
+      - name: "Add the General registry via Git"
+        run: |
+          import Pkg
+          ENV["JULIA_PKG_SERVER"] = ""
+          Pkg.Registry.add("General")
+        shell: julia --color=yes {0}
+      - name: "Install CompatHelper"
+        run: |
+          import Pkg
+          name = "CompatHelper"
+          uuid = "aa819f21-2bde-4658-8897-bab36330d9b7"
+          version = "3"
+          Pkg.add(; name, uuid, version)
+        shell: julia --color=yes {0}
+      - name: "Run CompatHelper"
+        run: |
+          import CompatHelper
+          CompatHelper.main()
+        shell: julia --color=yes {0}
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }}
-        run: julia -e 'using CompatHelper; CompatHelper.main()'

test/regression.jl

@@ -27,10 +27,7 @@
             observations = rand(prior) .+ σ .* randn(N)
 
             # define log likelihood function
-            ℓ(f) =
-                let observations = observations, σ = σ
-                    logpdf(MvNormal(f, σ), observations)
-                end
+            ℓ(f) = logpdf(MvNormal(f, σ^2 * I), observations)
 
             # run elliptical slice sampler for 100 000 time steps
             samples = sample(ESSModel(prior, ℓ), ESS(), 100_000; progress=false)
@@ -47,16 +44,13 @@
     # extreme case with independent observations
     @testset "Independent components" begin
         # define  distribution of latent variables
-        prior = MvNormal(N, 1)
+        prior = MvNormal(Zeros(N), I)
 
         # sample noisy observations
         observations = rand(prior) .+ σ .* randn(N)
 
         # define log likelihood function
-        ℓ(f) =
-            let observations = observations, σ = σ
-                logpdf(MvNormal(f, σ), observations)
-            end
+        ℓ(f) = logpdf(MvNormal(f, σ^2 * I), observations)
 
         # run elliptical slice sampling for 100 000 time steps
         samples = sample(ESSModel(prior, ℓ), ESS(), 100_000; progress=false)

test/runtests.jl

@@ -17,16 +17,18 @@ addprocs(2)
 @everywhere begin
     using EllipticalSliceSampling
     using Distributions
+
+    using LinearAlgebra
 end
 
 @testset "EllipticalSliceSampling" begin
     println("Simple tests")
-    @testset "Simple tests" begin
+    @time @testset "Simple tests" begin
         include("simple.jl")
     end
 
     println("GP regression tests")
-    @testset "GP regression tests" begin
+    @time @testset "GP regression tests" begin
         include("regression.jl")
     end
 end

test/simple.jl

@@ -2,10 +2,10 @@
 
     # Load all required packages and define likelihood functions
     ℓ(x) = logpdf(Normal(x, 0.5), 1.0)
-    ℓvec(x) = logpdf(MvNormal(x, 0.5), [1.0])
+    ℓvec(x) = logpdf(MvNormal(x, 0.25 * I), [1.0])
     @everywhere begin
         ℓ(x) = logpdf(Normal(x, 0.5), 1.0)
-        ℓvec(x) = logpdf(MvNormal(x, 0.5), [1.0])
+        ℓvec(x) = logpdf(MvNormal(x, 0.25 * I), [1.0])
     end
 
     @testset "Scalar model" begin
@@ -19,18 +19,15 @@
         σ² = 0.2
 
         # regular sampling
-        model = let prior = prior, ℓ = ℓ
-            ESSModel(prior, ℓ)
-        end
-        samples = sample(model, ESS(), 2_000; progress=false)
+        samples = sample(ESSModel(prior, ℓ), ESS(), 2_000; progress=false)
         @test samples isa Vector{Float64}
         @test length(samples) == 2_000
         @test mean(samples) ≈ μ atol = 0.05
         @test var(samples) ≈ σ² atol = 0.05
 
         # parallel sampling
         for alg in (MCMCThreads(), MCMCDistributed(), MCMCSerial())
-            samples = sample(model, ESS(), alg, 2_000, 5; progress=false)
+            samples = sample(ESSModel(prior, ℓ), ESS(), alg, 2_000, 5; progress=false)
             @test samples isa Vector{Vector{Float64}}
             @test length(samples) == 5
             @test all(length(x) == 2_000 for x in samples)
@@ -71,7 +68,7 @@
         Random.seed!(1)
 
         # model
-        prior = MvNormal([0.0], 1.0)
+        prior = MvNormal([0.0], I)
 
         # true posterior
         μ = [0.8]
@@ -100,7 +97,7 @@
         Random.seed!(1)
 
         # model
-        prior = MvNormal([0.5], 1.0)
+        prior = MvNormal([0.5], I)
 
         # true posterior
         μ = [0.9]