Add unified interface test utils

test/likelihoods/bernoulli.jl

@@ -1,13 +1,4 @@
 @testset "BernoulliLikelihood" begin
- rng = MersenneTwister(123)
- gp = GP(SqExponentialKernel())
- x = rand(rng, 10)
- y = rand(rng, 10)
  lik = BernoulliLikelihood()
- lgp = LatentGP(gp, lik, 1e-5)
- lfgp = lgp(x)
-
- @test typeof(lik(rand(rng, lfgp.fx))) <: Distribution
- @test length(rand(rng, lik(rand(rng, lfgp.fx)))) == 10
- @test Functors.functor(lik)[1] == ()
+ test_interface(lik, SqExponentialKernel(), rand(10))
 end

test/likelihoods/categorical.jl

@@ -1,22 +1,8 @@
 @testset "CategoricalLikelihood" begin
- rng = MersenneTwister(123)
- gp = GP(IndependentMOKernel(SqExponentialKernel()))
+ lik = CategoricalLikelihood()
  IN_DIM = 3
- OUT_DIM = 4
+ OUT_DIM = 2 # one for the mean the other for the log-standard deviation
  N = 10
- x = [rand(rng, IN_DIM) for _=1:N]
- X = MOInput(x, OUT_DIM)
- lik = CategoricalLikelihood()
- lgp = LatentGP(gp, lik, 1e-5)
- lfgp = lgp(X)
-
- Y = rand(rng, lfgp.fx)
-
- y = [Y[[i + j*N for j in 0:(OUT_DIM - 1)]] for i in 1:N]
- # Replace with mo_inverse_transform once it is merged
-
- @test length(lik(rand(3)).p) == 4
- @test lik(y) isa Distribution
- @test length(rand(rng, lik(y))) == 10
- @test Functors.functor(lik)[1] == ()
+ X = MOInput([rand(IN_DIM) for _ in 1:N], OUT_DIM)
+ test_interface(lik, IndependentMOKernel(SqExponentialKernel()), X)
 end

test/likelihoods/gaussian.jl

@@ -1,35 +1,13 @@
 @testset "GaussianLikelihood" begin
- rng = MersenneTwister(123)
- gp = GP(SqExponentialKernel())
- x = rand(rng, 10)
- y = rand(rng, 10)
  lik = GaussianLikelihood(1e-5)
- lgp = LatentGP(gp, lik, 1e-5)
- lfgp = lgp(x)
-
- @test lik(rand(rng, lfgp.fx)) isa Distribution
- @test length(rand(rng, lik(rand(rng, lfgp.fx)))) == 10
- @test keys(Functors.functor(lik)[1]) == (:σ²,)
+ test_interface(lik, SqExponentialKernel(), rand(10); functor_args=(:σ²,))
 end
 
 @testset "HeteroscedasticGaussianLikelihood" begin
- rng = MersenneTwister(123)
- gp = GP(IndependentMOKernel(SqExponentialKernel()))
+ lik = HeteroscedasticGaussianLikelihood()
  IN_DIM = 3
  OUT_DIM = 2 # one for the mean the other for the log-standard deviation
  N = 10
- x = [rand(rng, IN_DIM) for _ in 1:N]
- X = MOInput(x, OUT_DIM)
- lik = HeteroscedasticGaussianLikelihood()
- lgp = LatentGP(gp, lik, 1e-5)
- lfgp = lgp(X)
-
- Y = rand(rng, lfgp.fx)
-
- y = [Y[[i + j*N for j in 0:(OUT_DIM - 1)]] for i in 1:N]
- # Replace with mo_inverse_transform once it is merged
-
- @test lik(y) isa Distribution
- @test length(rand(rng, lik(y))) == 10
- @test Functors.functor(lik)[1] == ()
+ X = MOInput([rand(IN_DIM) for _ in 1:N], OUT_DIM)
+ test_interface(lik, IndependentMOKernel(SqExponentialKernel()), X)
 end

test/likelihoods/poisson.jl

@@ -1,13 +1,4 @@
 @testset "PoissonLikelihood" begin
- rng = MersenneTwister(123)
- gp = GP(SqExponentialKernel())
- x = rand(rng, 10)
- y = rand(rng, 10)
  lik = PoissonLikelihood()
- lgp = LatentGP(gp, lik, 1e-5)
- lfgp = lgp(x)
-
- @test lik(rand(rng, lfgp.fx)) isa Distribution
- @test length(rand(rng, lik(rand(rng, lfgp.fx)))) == 10
- @test Functors.functor(lik)[1] == ()
+ test_interface(lik, SqExponentialKernel(), rand(10))
 end

test/runtests.jl

@@ -7,6 +7,8 @@ using Distributions
 
 @testset "GPLikelihoods.jl" begin
 
+ include("test_utils.jl")
+
  @testset "likelihoods" begin
  include("likelihoods/bernoulli.jl")
  include("likelihoods/categorical.jl")

test/test_utils.jl

@@ -0,0 +1,60 @@
+function test_interface(
+ rng::AbstractRNG,
+ lik,
+ k::KernelFunctions.Kernel,
+ x::AbstractVector;
+ functor_args=(),
+)
+ gp = GP(k)
+ lgp = LatentGP(gp, lik, 1e-5)
+ lfgp = lgp(x)
+
+ # Check if likelihood produces a distribution
+ @test lik(rand(rng, lfgp.fx)) isa Distribution
+
+ N = length(x)
+ y = rand(rng, lfgp.fx)
+
+ if x isa MOInput
+ # TODO: replace with mo_inverse_transform
+ N = length(x.x)
+ y = [y[[i + j*N for j in 0:(x.out_dim - 1)]] for i in 1:N]
+ end
+
+ # Check if the likelihood samples are of correct length
+ @test length(rand(rng, lik(y))) == N
+
+ # Check if functor works properly
+ if functor_args == ()
+ @test Functors.functor(lik)[1] == functor_args
+ else
+ @test keys(Functors.functor(lik)[1]) == functor_args
+ end
+end
+
+"""
+ test_interface(
+ lik,
+ k::Kernel,
+ x::AbstractVector;
+ functor_args=(),
+ )
+
+This function provides unified method to check the interface of the various likelihoods 
+defined. It checks if the likelihood produces a distribution, length of likelihood 
+samples is correct and if the functor works as intended. 
+"""
+function test_interface(
+ lik,
+ k::KernelFunctions.Kernel,
+ x::AbstractVector;
+ kwargs...
+)
+ test_interface(
+ Random.GLOBAL_RNG,
+ lik,
+ k,
+ x;
+ kwargs...
+ )
+end