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Simplify tests
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@devmotion
devmotion committed Jun 30, 2023
1 parent 1529164 commit 23d69d4
Showing 1 changed file with 43 additions and 58 deletions.
101 changes: 43 additions & 58 deletions test/multivariate/multinomial.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,11 @@ using Distributions, Random, StaticArrays
using Test


@testset "testing Multinomial with type $type" for (type, tol) in zip((Float16, Float32, Float64), (1e-3, 1e-6, 1e-8))
p = [0.2, 0.5, 0.3]
@testset "testing Multinomial with type $T" for T in (Float16, Float32, Float64)
p = T[0.2, 0.5, 0.3]
nt = 10
rng = MersenneTwister(123)
d = Multinomial(nt, convert.(type, p))
d = Multinomial(nt, p)

@testset "Testing Multinomial with $key" for (key, func) in
Dict("rand(...)" => [rand, rand],
Expand All @@ -18,28 +18,29 @@ using Test

@test length(d) == 3
@test d.n == nt
@test mean(d) ≈ [2., 5., 3.]
@test var(d) ≈ [1.6, 2.5, 2.1]
@test cov(d) ≈ [1.6 -1.0 -0.6; -1.0 2.5 -1.5; -0.6 -1.5 2.1]
@test mean(d) ≈ T[2., 5., 3.]
@test var(d) ≈ T[1.6, 2.5, 2.1]
@test cov(d) ≈ T[1.6 -1.0 -0.6; -1.0 2.5 -1.5; -0.6 -1.5 2.1]

@test insupport(d, [1, 6, 3])
@test !insupport(d, [2, 6, 3])
@test partype(d) == type #Float64
@test partype(Multinomial(nt, Vector{Float32}(p))) == Float32
@test partype(d) === T

# Conversion
@test typeof(d) == Multinomial{type, Vector{type}}
@test typeof(Multinomial(nt, Vector{Float32}(p))) == Multinomial{Float32, Vector{Float32}}
@test typeof(convert(Multinomial{Float32}, d)) == Multinomial{Float32, Vector{Float32}}
@test typeof(convert(Multinomial{Float32, Vector{Float32}}, d)) == Multinomial{Float32, Vector{Float32}}
@test typeof(convert(Multinomial{Float32}, params(d)...)) == Multinomial{Float32, Vector{Float32}}
@test typeof(convert(Multinomial{Float32, Vector{Float32}}, params(d)...)) == Multinomial{Float32, Vector{Float32}}
@test convert(Multinomial{type}, d) === d
@test convert(Multinomial{type, Vector{type}}, d) === d
@test typeof(d) === Multinomial{T, Vector{T}}
for S in (Float16, Float32, Float64)
S === T && continue
@test typeof(convert(Multinomial{S}, d)) === Multinomial{S, Vector{S}}
@test typeof(convert(Multinomial{S, Vector{S}}, d)) === Multinomial{S, Vector{S}}
@test typeof(convert(Multinomial{S}, params(d)...)) === Multinomial{S, Vector{S}}
@test typeof(convert(Multinomial{S, Vector{S}}, params(d)...)) == Multinomial{S, Vector{S}}
end
@test convert(Multinomial{T}, d) === d
@test convert(Multinomial{T, Vector{T}}, d) === d

# random sampling

x = func[1](d)
x = @inferred(func[1](d))
@test isa(x, Vector{Int})
@test sum(x) == nt
@test insupport(d, x)
Expand All @@ -48,12 +49,12 @@ using Test
@test d == typeof(d)(params(d)...)
@test d == deepcopy(d)

x = func[2](d, 100)
x = @inferred(func[2](d, 100))
@test isa(x, Matrix{Int})
@test all(sum(x, dims=1) .== nt)
@test all(insupport(d, x))

x = func[1](sampler(d))
x = @inferred(func[1](sampler(d)))
@test isa(x, Vector{Int})
@test sum(x) == nt
@test insupport(d, x)
Expand All @@ -62,8 +63,8 @@ using Test

x1 = [1, 6, 3]

@test isapprox(pdf(d, x1), 0.070875, atol=tol)
@test logpdf(d, x1) ≈ log(pdf(d, x1))
@test @inferred(pdf(d, x1)) ≈ T(0.070875)
@test @inferred(logpdf(d, x1)) ≈ log(pdf(d, x1))

x = func[2](d, 100)
pv = pdf(d, x)
Expand All @@ -73,17 +74,17 @@ using Test
@test lp[i] ≈ logpdf(d, x[:,i])
end

# test type stability of logpdf
@test typeof(logpdf(convert(Multinomial{Float32}, d), x1)) == Float32
# test result type of logpdf
@test typeof(logpdf(d, x1)) === T

# test degenerate cases of logpdf
d1 = Multinomial(1, [0.5, 0.5, 0.0])
d2 = Multinomial(0, [0.5, 0.5, 0.0])
d1 = Multinomial(1, T[0.5, 0.5, 0.0])
d2 = Multinomial(0, T[0.5, 0.5, 0.0])
x2 = [1, 0, 0]
x3 = [0, 0, 1]
x4 = [1, 0, 1]

@test logpdf(d1, x2) ≈ log(0.5)
@test logpdf(d1, x2) ≈ T(log(0.5))
@test logpdf(d2, x2) == -Inf
@test logpdf(d1, x3) == -Inf
@test logpdf(d2, x3) == -Inf
Expand All @@ -98,13 +99,13 @@ using Test
ss = suffstats(Multinomial, x)
@test isa(ss, Distributions.MultinomialStats)
@test ss.n == nt
@test ss.scnts == vec(sum(Float64[x[i,j] for i = 1:size(x,1), j = 1:size(x,2)], dims=2))
@test ss.scnts == vec(sum(T[x[i,j] for i = 1:size(x,1), j = 1:size(x,2)], dims=2))
@test ss.tw == n0

ss = suffstats(Multinomial, x, w)
@test isa(ss, Distributions.MultinomialStats)
@test ss.n == nt
@test ss.scnts ≈ Float64[x[i,j] for i = 1:size(x,1), j = 1:size(x,2)] * w
@test ss.scnts ≈ T[x[i,j] for i = 1:size(x,1), j = 1:size(x,2)] * w
@test ss.tw ≈ sum(w)

# fit
Expand All @@ -116,16 +117,16 @@ using Test
r = fit(Multinomial, x)
@test r.n == nt
@test length(r) == length(p)
@test isapprox(probs(r), p, atol=0.02)
@test probs(r) ≈ p atol=0.02
r = fit(Multinomial{Float64}, x)
@test r.n == nt
@test length(r) == length(p)
@test isapprox(probs(r), p, atol=0.02)
@test probs(r) ≈ p atol=0.02

r = fit_mle(Multinomial, x, fill(2.0, size(x,2)))
@test r.n == nt
@test length(r) == length(p)
@test isapprox(probs(r), p, atol=0.02)
@test probs(r) ≈ p atol=0.02

# behavior for n = 0
d0 = Multinomial(0, p)
Expand All @@ -143,7 +144,7 @@ using Test

# Abstract vector p

@test typeof(Multinomial(nt, SVector{length(p), Float64}(p))) == Multinomial{Float64, SVector{3, Float64}}
@test typeof(Multinomial(nt, SVector{length(p), T}(p))) == Multinomial{T, SVector{3, T}}

end

Expand All @@ -152,7 +153,7 @@ using Test
"rand!(rng, ...)" => (dist, X) -> rand!(rng, dist, X))
# random sampling
X = Matrix{Int}(undef, length(p), 100)
x = func(d, X)
x = @inferred(func(d, X))
@test x ≡ X
@test isa(x, Matrix{Int})
@test all(sum(x, dims=1) .== nt)
Expand All @@ -170,7 +171,7 @@ using Test
"rand!(rng, ..., true)" => (dist, X) -> rand!(rng, dist, X, true))
# random sampling
X = Vector{Vector{Int}}(undef, 100)
x = func(d, X)
x = @inferred(func(d, X))
@test x ≡ X
@test all(sum.(x) .== nt)
@test all(insupport(d, a) for a in x)
Expand All @@ -182,49 +183,33 @@ using Test
# random sampling
X = [Vector{Int}(undef, length(p)) for _ in Base.OneTo(100)]
x1 = X[1]
x = func(d, X)
x = @inferred(func(d, X))
@test x1 ≡ X[1]
@test all(sum.(x) .== nt)
@test all(insupport(d, a) for a in x)
end

repeats = 10
m = Vector{Vector{partype(d)}}(undef, repeats)
rand!(d, m)
m = Vector{Vector{T}}(undef, repeats)
@inferred(rand!(d, m))
@test isassigned(m, 1)
m1=m[1]
rand!(d, m)
@inferred(rand!(d, m))
@test m1 ≡ m[1]
rand!(d, m, true)
@inferred(rand!(d, m, true))
@test m1 ≢ m[1]
m1 = m[1]
rand!(d, m, false)
@inferred(rand!(d, m, false))
@test m1 ≡ m[1]

p = [0.2, 0.4, 0.3, 0.1]
p = T[0.2, 0.4, 0.3, 0.1]
nt = 10
d = Multinomial(nt, p)
@test_throws DimensionMismatch rand!(d, m, false)
@test_nowarn rand!(d, m)

p_v = [0.1, 0.4, 0.3, 0.8]
p_v = T[0.1, 0.4, 0.3, 0.8]
@test_throws DomainError Multinomial(10, p_v)
@test_throws DomainError Multinomial(10, p_v; check_args=true)
Multinomial(10, p_v; check_args=false) # should not warn
end

@testset "Type stability" begin

p = [0.2, 0.5, 0.3]

@inferred rand(Multinomial(10, convert.(Float32, p)))
@inferred rand(Multinomial(10, convert.(Float64, p)))
@inferred rand(Multinomial(10, convert.(Float16, p)))

x = zeros(Float64, size(p)...)
@inferred rand!(Multinomial(10, convert.(Float32, p)), convert.(Float32, x))
@inferred rand!(Multinomial(10, convert.(Float32, p)), convert.(Float64, x))
@inferred rand!(Multinomial(10, convert.(Float32, p)), convert.(Float16, x))
end


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