Simplify Testing and Deps (#41)

* Remove old code

* Remove unnecessary deps

* Bump patch version

* Test on lts and 1

* Update workflow

* Formatting

* Actions upgrade

Author: willtebbutt

.github/workflows/CI.yml

@@ -16,7 +16,7 @@ jobs:
       matrix:
         version:
           - '1'
-          - '1.6'
+          - 'lts'
         os:
           - ubuntu-latest
         arch:
@@ -27,29 +27,18 @@ jobs:
             arch: x64
             coverage: true
     steps:
-      - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v1
-        env:
-          cache-name: cache-artifacts
-        with:
-          path: ~/.julia/artifacts
-          key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
-          restore-keys: |
-            ${{ runner.os }}-test-${{ env.cache-name }}-
-            ${{ runner.os }}-test-
-            ${{ runner.os }}-
+          include-all-prereleases: false
+      - uses: julia-actions/cache@v2
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
-        with:
-          coverage: ${{ matrix.coverage || false }}
       - uses: julia-actions/julia-processcoverage@v1
-        if: matrix.coverage
-      - name: Send coverage
-        if: matrix.coverage
-        uses: codecov/codecov-action@v2
+      - uses: codecov/codecov-action@v5
         with:
-          file: lcov.info
+          files: lcov.info
+          token: ${{ secrets.CODECOV_TOKEN }}
+          fail_ci_if_error: false

Project.toml

@@ -1,7 +1,7 @@
 name = "BayesianLinearRegressors"
 uuid = "f579363c-4606-5e5c-a623-c4549f609c4b"
-authors = ["Will Tebbutt <[email protected]>"]
-version = "0.3.8"
+authors = ["Will Tebbutt"]
+version = "0.3.9"
 
 [deps]
 AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
@@ -11,6 +11,14 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
-AbstractGPs = "0.3, 0.4, 0.5"
+AbstractGPs = "0.5"
+LinearAlgebra = "1"
 PDMats = "0.11"
-julia = "1.3"
+Test = "1"
+julia = "1.10"
+
+[extras]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Test"]

src/BayesianLinearRegressors.jl

@@ -1,10 +1,6 @@
 module BayesianLinearRegressors
 
-using AbstractGPs
-using LinearAlgebra
-using Random
-using Statistics
-using PDMats
+using AbstractGPs, LinearAlgebra, PDMats, Random
 
 using AbstractGPs: AbstractGP, _cholesky, FiniteGP
 

test/Project.toml

@@ -18,39 +18,13 @@
             Σ_empirical = (Y .- mean(Y; dims=2)) * (Y .- mean(Y; dims=2))' ./ samples
             @test mean(f(X, Σy)) ≈ m_empirical atol = 1e-2 rtol = 1e-2
             @test cov(f(X, Σy)) ≈ Σ_empirical atol = 1e-2 rtol = 1e-2
-
-            @testset "Zygote (everything dense)" begin
-                function rand_blr(X, A_Σy, mw, A_Λw)
-                    Σy, Λw = Symmetric(A_Σy * A_Σy' + I), Symmetric(A_Λw * A_Λw' + I)
-                    f = BayesianLinearRegressor(mw, Λw)
-                    return rand(MersenneTwister(123456), f(X, Σy), 3)
-                end
-                mw, A_Σy, A_Λw = f.mw, 0.1 .* randn(rng, N, N), 0.1 .* randn(rng, D, D)
-
-                # Run the model forwards and check that output agrees with non-Zygote output
-                z, back = Zygote.pullback(rand_blr, X, A_Σy, mw, A_Λw)
-                @test z == rand_blr(X, A_Σy, mw, A_Λw)
-
-                # Compute adjoints using Zygote.
-                z̄ = randn(rng, size(z))
-                dX, dA_Σy, dmw, dA_Λw = back(z̄)
-
-                # Verify adjoints via finite differencing.
-                fdm = central_fdm(5, 1)
-                @test dX ≈ first(j′vp(fdm, X -> rand_blr(X, A_Σy, mw, A_Λw), z̄, X))
-                @test dA_Σy ≈
-                    first(j′vp(fdm, A_Σy -> rand_blr(X, A_Σy, mw, A_Λw), z̄, A_Σy))
-                @test dmw ≈ first(j′vp(fdm, mw -> rand_blr(X, A_Σy, mw, A_Λw), z̄, mw))
-                @test dA_Λw ≈
-                    first(j′vp(fdm, A_Λw -> rand_blr(X, A_Σy, mw, A_Λw), z̄, A_Λw))
-            end
         end
         @testset "logpdf" begin
             rng, N, D = MersenneTwister(123456), 13, 7
             X, f, Σy = generate_toy_problem(rng, N, D, Tx)
             y = rand(rng, f(X, Σy))
 
-            # Construct MvNormal using a naive but simple computation for the mean / cov.
+            # Compute logpdf using a naive but simple computation for the mean / cov.
             function naive_normal_stats(X::Matrix)
                 return (X' * f.mw, Symmetric(X' * (cholesky(f.Λw) \ X) + Σy))
             end
@@ -59,33 +33,8 @@
             m, Σ = naive_normal_stats(X)
 
             # Check that logpdf agrees between distributions and BLR.
-            @test logpdf(f(X, Σy), y) ≈ logpdf(MvNormal(m, Σ), y)
-
-            @testset "Zygote (everything dense)" begin
-                function logpdf_blr(X, A_Σy, y, mw, A_Λw)
-                    Σy, Λw = Symmetric(A_Σy * A_Σy' + I), Symmetric(A_Λw * A_Λw' + I)
-                    f = BayesianLinearRegressor(mw, Λw)
-                    return logpdf(f(X, Σy), y)
-                end
-                mw, A_Σy, A_Λw = f.mw, 0.1 .* randn(rng, N, N), 0.1 .* randn(rng, D, D)
-
-                z, back = Zygote.pullback(logpdf_blr, X, A_Σy, y, mw, A_Λw)
-                @test z == logpdf_blr(X, A_Σy, y, mw, A_Λw)
-
-                # Compute gradients using Zygote.
-                z̄ = randn(rng)
-                dX, dA_Σy, dy, dmw, dA_Λw = back(z̄)
-
-                # Check correctness via finite differencing.
-                fdm = central_fdm(5, 1)
-                @test dX ≈ first(j′vp(fdm, X -> logpdf_blr(X, A_Σy, y, mw, A_Λw), z̄, X))
-                @test dA_Σy ≈
-                    first(j′vp(fdm, A_Σy -> logpdf_blr(X, A_Σy, y, mw, A_Λw), z̄, A_Σy))
-                @test dy ≈ first(j′vp(fdm, y -> logpdf_blr(X, A_Σy, y, mw, A_Λw), z̄, y))
-                @test dmw ≈ first(j′vp(fdm, mw -> logpdf_blr(X, A_Σy, y, mw, A_Λw), z̄, mw))
-                @test dA_Λw ≈
-                    first(j′vp(fdm, A_Λw -> logpdf_blr(X, A_Σy, y, mw, A_Λw), z̄, A_Λw))
-            end
+            δ = y - m
+            @test logpdf(f(X, Σy), y) ≈ -(N * log(2π) + logdet(Σ) + δ' * (Σ \ δ)) / 2
         end
         @testset "posterior" begin
             @testset "low noise" begin

test/bayesian_linear_regression.jl

@@ -1,15 +1,6 @@
-using AbstractGPs
-using BayesianLinearRegressors
-using Distributions
-using FiniteDifferences
-using LinearAlgebra
-using Random
-using Test
-using Zygote
-using PDMats
+using AbstractGPs, BayesianLinearRegressors, LinearAlgebra, PDMats, Random, Test
 
 using BayesianLinearRegressors: BayesianLinearRegressor, posterior, marginals, cov, mean
-using FiniteDifferences: j′vp
 
 include("test_utils.jl")
 

test/runtests.jl

@@ -44,46 +44,6 @@
                 @test mean(f(X, Σy)) ≈ m_empirical atol = 1e-3 rtol = 1e-3
                 @test cov(f(X, Σy)) ≈ Σ_empirical + Σy atol = 1e-2 rtol = 1e-2
             end
-
-            @testset "Zygote (everything dense)" begin
-                function test_rand_funcs_adjoints(sample_function)
-                    rng, N, D = MersenneTwister(123456), 11, 5
-                    X, f, _ = generate_toy_problem(rng, N, D, Tx)
-                    mw, A_Λw = f.mw, 0.1 .* randn(rng, D, D)
-
-                    # Run the model forwards and check that output agrees with non-Zygote.
-                    z, back = Zygote.pullback(sample_function, X, mw, A_Λw)
-                    @test z == sample_function(X, mw, A_Λw)
-
-                    # Compute adjoints using Zygote.
-                    z̄ = randn(rng, size(z))
-                    dX, dmw, dA_Λw = back(z̄)
-
-                    # Verify adjoints via finite differencing.
-                    fdm = central_fdm(5, 1)
-                    @test dX ≈ first(j′vp(fdm, X -> sample_function(X, mw, A_Λw), z̄, X))
-                    @test dmw ≈ first(j′vp(fdm, mw -> sample_function(X, mw, A_Λw), z̄, mw))
-                    @test dA_Λw ≈
-                        first(j′vp(fdm, A_Λw -> sample_function(X, mw, A_Λw), z̄, A_Λw))
-                end
-
-                function rand_funcs_single(X, mw, A_Λw)
-                    Λw = Symmetric(A_Λw * A_Λw' + I)
-                    f = BayesianLinearRegressor(mw, Λw)
-                    g = rand(MersenneTwister(123456), f)
-                    return g(X)
-                end
-
-                function rand_funcs_multi(X, mw, A_Λw)
-                    Λw = Symmetric(A_Λw * A_Λw' + I)
-                    f = BayesianLinearRegressor(mw, Λw)
-                    gs = rand(MersenneTwister(123456), f, 1, 1)
-                    return reduce(hcat, map(h -> h(X), reshape(gs, :)))
-                end
-
-                test_rand_funcs_adjoints(rand_funcs_single)
-                test_rand_funcs_adjoints(rand_funcs_multi)
-            end
         end
 
         @testset "basis_function_regression" begin