Saved version of the old tests involving AbstractGPs and GPLikelihoods. To be moved to AbstractGPs or ApproximateGPs

TestInterface.jl

module TestInterface

using Random
using Test

function test_interface(
rng::AbstractRNG, lik, x::AbstractVector; functor_args=()
)
gp = GP(kernel)
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
xs, re = Functors.functor(lik)
@test lik == re(xs)
if isempty(functor_args)
    @test xs === ()
else
    @test keys(xs) == functor_args
end

return nothing
end

@doc raw"""
    test_interface([rng::AbstractRNG,] lik, k::Kernel, x::AbstractVector; functor_args=())

!!! warning

    KernelFunctions.jl and AbstractGPs.jl need to be loaded in the current workspace
    to run this function.

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.

...
# Arguments
- `lik`: the likelihood to test the interface of
- `k::Kernel`: the kernel to use for the GP
- `x::AbstractVector`: inputs to compute the likelihood on
- `functor_args=()`: a collection of symbols of arguments to match functor parameters with.
...
"""
function test_interface(lik, kernel, x::AbstractVector; kwargs...)
    return test_interface(Random.GLOBAL_RNG, lik, kernel, x; kwargs...)
end


end