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Move test utilities to an extension (#1791)
* Move test utilities to an extension * Fix signature and docstring * Also qualify AbstractRNG * Fix Julia < 1.9 * Fix for 1.3? * Simplify the TestUtils stub
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,101 @@ | ||
| module DistributionsTestExt | ||
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| using Distributions | ||
| using Distributions.LinearAlgebra | ||
| using Distributions.Random | ||
| using Test | ||
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| __rand(::Nothing, args...) = rand(args...) | ||
| __rand(rng::AbstractRNG, args...) = rand(rng, args...) | ||
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| __rand!(::Nothing, args...) = rand!(args...) | ||
| __rand!(rng::AbstractRNG, args...) = rand!(rng, args...) | ||
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| """ | ||
| test_mvnormal( | ||
| g::AbstractMvNormal, | ||
| n_tsamples::Int=10^6, | ||
| rng::Union{Random.AbstractRNG, Nothing}=nothing, | ||
| ) | ||
| Test that `AbstractMvNormal` implements the expected API. | ||
| !!! Note | ||
| On Julia >= 1.9, you have to load the `Test` standard library to be able to use | ||
| this function. | ||
| """ | ||
| function Distributions.TestUtils.test_mvnormal( | ||
| g::AbstractMvNormal, n_tsamples::Int=10^6, rng::Union{AbstractRNG, Nothing}=nothing | ||
| ) | ||
| d = length(g) | ||
| μ = mean(g) | ||
| Σ = cov(g) | ||
| @test length(μ) == d | ||
| @test size(Σ) == (d, d) | ||
| @test var(g) ≈ diag(Σ) | ||
| @test entropy(g) ≈ 0.5 * logdet(2π * ℯ * Σ) | ||
| ldcov = logdetcov(g) | ||
| @test ldcov ≈ logdet(Σ) | ||
| vs = diag(Σ) | ||
| @test g == typeof(g)(params(g)...) | ||
| @test g == deepcopy(g) | ||
| @test minimum(g) == fill(-Inf, d) | ||
| @test maximum(g) == fill(Inf, d) | ||
| @test extrema(g) == (minimum(g), maximum(g)) | ||
| @test isless(extrema(g)...) | ||
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| # test sampling for AbstractMatrix (here, a SubArray): | ||
| subX = view(__rand(rng, d, 2d), :, 1:d) | ||
| @test isa(__rand!(rng, g, subX), SubArray) | ||
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| # sampling | ||
| @test isa(__rand(rng, g), Vector{Float64}) | ||
| X = __rand(rng, g, n_tsamples) | ||
| emp_mu = vec(mean(X, dims=2)) | ||
| Z = X .- emp_mu | ||
| emp_cov = (Z * Z') * inv(n_tsamples) | ||
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| mean_atols = 8 .* sqrt.(vs ./ n_tsamples) | ||
| cov_atols = 10 .* sqrt.(vs .* vs') ./ sqrt.(n_tsamples) | ||
| for i = 1:d | ||
| @test isapprox(emp_mu[i], μ[i], atol=mean_atols[i]) | ||
| end | ||
| for i = 1:d, j = 1:d | ||
| @test isapprox(emp_cov[i,j], Σ[i,j], atol=cov_atols[i,j]) | ||
| end | ||
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| X = rand(MersenneTwister(14), g, n_tsamples) | ||
| Y = rand(MersenneTwister(14), g, n_tsamples) | ||
| @test X == Y | ||
| emp_mu = vec(mean(X, dims=2)) | ||
| Z = X .- emp_mu | ||
| emp_cov = (Z * Z') * inv(n_tsamples) | ||
| for i = 1:d | ||
| @test isapprox(emp_mu[i] , μ[i] , atol=mean_atols[i]) | ||
| end | ||
| for i = 1:d, j = 1:d | ||
| @test isapprox(emp_cov[i,j], Σ[i,j], atol=cov_atols[i,j]) | ||
| end | ||
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| # evaluation of sqmahal & logpdf | ||
| U = X .- μ | ||
| sqm = vec(sum(U .* (Σ \ U), dims=1)) | ||
| for i = 1:min(100, n_tsamples) | ||
| @test sqmahal(g, X[:,i]) ≈ sqm[i] | ||
| end | ||
| @test sqmahal(g, X) ≈ sqm | ||
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| lp = -0.5 .* sqm .- 0.5 * (d * log(2.0 * pi) + ldcov) | ||
| for i = 1:min(100, n_tsamples) | ||
| @test logpdf(g, X[:,i]) ≈ lp[i] | ||
| end | ||
| @test logpdf(g, X) ≈ lp | ||
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| # log likelihood | ||
| @test loglikelihood(g, X) ≈ sum(i -> Distributions._logpdf(g, X[:,i]), 1:n_tsamples) | ||
| @test loglikelihood(g, X[:, 1]) ≈ logpdf(g, X[:, 1]) | ||
| @test loglikelihood(g, [X[:, i] for i in axes(X, 2)]) ≈ loglikelihood(g, X) | ||
| end | ||
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| end # module |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,96 +1,19 @@ | ||
| module TestUtils | ||
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| using Distributions | ||
| using LinearAlgebra | ||
| using Random | ||
| using Test | ||
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| __rand(::Nothing, args...) = rand(args...) | ||
| __rand(rng::AbstractRNG, args...) = rand(rng, args...) | ||
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| __rand!(::Nothing, args...) = rand!(args...) | ||
| __rand!(rng::AbstractRNG, args...) = rand!(rng, args...) | ||
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| """ | ||
| test_mvnormal( | ||
| g::AbstractMvNormal, n_tsamples::Int=10^6, rng::AbstractRNG=Random.default_rng() | ||
| ) | ||
| Test that `AbstractMvNormal` implements the expected API. | ||
| """ | ||
| function test_mvnormal( | ||
| g::AbstractMvNormal, n_tsamples::Int=10^6, rng::Union{AbstractRNG, Nothing}=nothing | ||
| ) | ||
| d = length(g) | ||
| μ = mean(g) | ||
| Σ = cov(g) | ||
| @test length(μ) == d | ||
| @test size(Σ) == (d, d) | ||
| @test var(g) ≈ diag(Σ) | ||
| @test entropy(g) ≈ 0.5 * logdet(2π * ℯ * Σ) | ||
| ldcov = logdetcov(g) | ||
| @test ldcov ≈ logdet(Σ) | ||
| vs = diag(Σ) | ||
| @test g == typeof(g)(params(g)...) | ||
| @test g == deepcopy(g) | ||
| @test minimum(g) == fill(-Inf, d) | ||
| @test maximum(g) == fill(Inf, d) | ||
| @test extrema(g) == (minimum(g), maximum(g)) | ||
| @test isless(extrema(g)...) | ||
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| # test sampling for AbstractMatrix (here, a SubArray): | ||
| subX = view(__rand(rng, d, 2d), :, 1:d) | ||
| @test isa(__rand!(rng, g, subX), SubArray) | ||
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| # sampling | ||
| @test isa(__rand(rng, g), Vector{Float64}) | ||
| X = __rand(rng, g, n_tsamples) | ||
| emp_mu = vec(mean(X, dims=2)) | ||
| Z = X .- emp_mu | ||
| emp_cov = (Z * Z') * inv(n_tsamples) | ||
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| mean_atols = 8 .* sqrt.(vs ./ n_tsamples) | ||
| cov_atols = 10 .* sqrt.(vs .* vs') ./ sqrt.(n_tsamples) | ||
| for i = 1:d | ||
| @test isapprox(emp_mu[i], μ[i], atol=mean_atols[i]) | ||
| end | ||
| for i = 1:d, j = 1:d | ||
| @test isapprox(emp_cov[i,j], Σ[i,j], atol=cov_atols[i,j]) | ||
| import ..Distributions | ||
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| function test_mvnormal end | ||
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| if isdefined(Base, :get_extension) && isdefined(Base.Experimental, :register_error_hint) | ||
| function __init__() | ||
| # Better error message if users forget to load Test | ||
| Base.Experimental.register_error_hint(MethodError) do io, exc, _, _ | ||
| if exc.f === test_mvnormal && | ||
| (Base.get_extension(Distributions, :DistributionsTestExt) === nothing) | ||
| print(io, "\nDid you forget to load Test?") | ||
| end | ||
| end | ||
| end | ||
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| X = rand(MersenneTwister(14), g, n_tsamples) | ||
| Y = rand(MersenneTwister(14), g, n_tsamples) | ||
| @test X == Y | ||
| emp_mu = vec(mean(X, dims=2)) | ||
| Z = X .- emp_mu | ||
| emp_cov = (Z * Z') * inv(n_tsamples) | ||
| for i = 1:d | ||
| @test isapprox(emp_mu[i] , μ[i] , atol=mean_atols[i]) | ||
| end | ||
| for i = 1:d, j = 1:d | ||
| @test isapprox(emp_cov[i,j], Σ[i,j], atol=cov_atols[i,j]) | ||
| end | ||
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| # evaluation of sqmahal & logpdf | ||
| U = X .- μ | ||
| sqm = vec(sum(U .* (Σ \ U), dims=1)) | ||
| for i = 1:min(100, n_tsamples) | ||
| @test sqmahal(g, X[:,i]) ≈ sqm[i] | ||
| end | ||
| @test sqmahal(g, X) ≈ sqm | ||
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| lp = -0.5 .* sqm .- 0.5 * (d * log(2.0 * pi) + ldcov) | ||
| for i = 1:min(100, n_tsamples) | ||
| @test logpdf(g, X[:,i]) ≈ lp[i] | ||
| end | ||
| @test logpdf(g, X) ≈ lp | ||
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| # log likelihood | ||
| @test loglikelihood(g, X) ≈ sum(i -> Distributions._logpdf(g, X[:,i]), 1:n_tsamples) | ||
| @test loglikelihood(g, X[:, 1]) ≈ logpdf(g, X[:, 1]) | ||
| @test loglikelihood(g, [X[:, i] for i in axes(X, 2)]) ≈ loglikelihood(g, X) | ||
| end | ||
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| end |