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Merged
ChrisRackauckas merged 1 commit into from
Jun 14, 2026
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Use SciMLTesting v1.2 (folder-based run_tests) #85

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8 changes: 5 additions & 3 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -14,9 +14,10 @@ Distributions = "0.25"
ExplicitImports = "1.14.0"
JET = "0.9, 0.10, 0.11"
LogExpFunctions = "0.3.14, 1.0"
Pkg = "1"
PrecompileTools = "1.2.0"
Random = "1.10"
SafeTestsets = "0.1, 1"
SciMLTesting = "1"
Statistics = "1"
Test = "1"
julia = "1.10"
Expand All @@ -27,9 +28,10 @@ Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
SciMLTesting = "09d9d899-5365-40a9-917a-5f67fddea283"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[targets]
test = ["AllocCheck", "Aqua", "Statistics", "Test", "Distributions", "JET", "ExplicitImports", "Pkg"]
test = ["AllocCheck", "Aqua", "Statistics", "Test", "Distributions", "JET", "ExplicitImports", "SafeTestsets", "SciMLTesting"]
25 changes: 25 additions & 0 deletions test/bigfloat_tests.jl
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@@ -0,0 +1,25 @@
using PoissonRandom
using Statistics
using Test

@testset "count_rand with BigFloat (λ < 6)" begin
for _ in 1:100
result = pois_rand(BigFloat(3.0))
@test result isa Integer
@test result >= 0
end
end
@testset "ad_rand with BigFloat (λ >= 6)" begin
for _ in 1:100
result = pois_rand(BigFloat(15.0))
@test result isa Integer
@test result >= 0
end
end
@testset "statistical validity with BigFloat" begin
n = 10000
λ = BigFloat(10.0)
samples = [pois_rand(λ) for _ in 1:n]
sample_mean = mean(samples)
@test abs(sample_mean - Float64(λ)) < 3 * sqrt(Float64(λ))
end
16 changes: 16 additions & 0 deletions test/passthrough_rng_tests.jl
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@@ -0,0 +1,16 @@
using PoissonRandom
using Random: Random, UInt52Raw
using Test

prng = PassthroughRNG()
# The CUDA.jl @device_override Random.randexp(::AbstractRNG) shadows our
# specific Random.randexp(::PassthroughRNG) on the GPU because Julia's
# OverlayMethodTable returns overlay matches without consulting the base
# table when the overlay fully covers the signature. The override body
# then calls these against PassthroughRNG; if they MethodError, kernel
# compilation fails with InvalidIRError on jl_f_throw_methoderror.
@test Random.rng_native_52(prng) === UInt64
@test Random.rand(prng, UInt52Raw()) isa UInt64
@test Random.rand(prng, UInt64) isa UInt64
@test Random.rand(prng, Float32) isa Float32
@test Random.rand(prng, Float64) isa Float64
4 changes: 4 additions & 0 deletions test/qa/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,8 @@ ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
PoissonRandom = "e409e4f3-bfea-5376-8464-e040bb5c01ab"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
SciMLTesting = "09d9d899-5365-40a9-917a-5f67fddea283"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[sources]
Expand All @@ -14,5 +16,7 @@ Aqua = "0.8"
ExplicitImports = "1.14.0"
JET = "0.9, 0.10, 0.11"
Random = "1.10"
SafeTestsets = "0.1, 1"
SciMLTesting = "1"
Test = "1"
julia = "1.10"
147 changes: 2 additions & 145 deletions test/runtests.jl
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@@ -1,145 +1,2 @@
using PoissonRandom
import Distributions
using Test, Statistics

const GROUP = get(ENV, "GROUP", "All")

if GROUP == "QA"
using Pkg
Pkg.activate(joinpath(@__DIR__, "qa"))
Pkg.develop(Pkg.PackageSpec(path = joinpath(@__DIR__, "..")))
Pkg.instantiate()
include(joinpath(@__DIR__, "qa", "qa.jl"))
else
n_tsamples = 10^5

function test_samples(
rand_func,
distr::Distributions.DiscreteUnivariateDistribution,
n::Int; # number of samples to generate
q::Float64 = 1.0e-8, # confidence interval, 1 - q as confidence
verbose::Bool = false
) # show intermediate info (for debugging)

# The basic idea
# ------------------
# Generate n samples, and count the occurrences of each value within a reasonable range.
# For each distinct value, it computes an confidence interval of the counts
# and checks whether the count is within this interval.
#
# If the distribution has a bounded range, it also checks whether
# the samples are all within this range.
#
# By setting a small q, we ensure that failure of the tests rarely
# happen in practice.
#
λ = distr.λ
n > 1 || error("The number of samples must be greater than 1.")
0.0 < q < 0.1 || error("The value of q must be within the open interval (0.0, 0.1).")

# determine the range of values to examine
vmin = minimum(distr)
vmax = maximum(distr)

rmin = floor(Int, quantile(distr, 0.00001))::Int
rmax = floor(Int, quantile(distr, 0.99999))::Int
m = rmax - rmin + 1 # length of the range
p0 = Distributions.pdf.((distr,), rmin:rmax) # reference probability masses
@assert length(p0) == m

# determine confidence intervals for counts:
# with probability q, the count will be out of this interval.
#
clb = Vector{Int}(undef, m)
cub = Vector{Int}(undef, m)
for i in 1:m
bp = Distributions.Binomial(n, p0[i])
clb[i] = floor(Int, quantile(bp, q / 2))
cub[i] = ceil(Int, Distributions.cquantile(bp, q / 2))
@assert cub[i] >= clb[i]
end

# generate samples
samples = [rand_func(λ) for i in 1:n]
@assert length(samples) == n

# scan samples and get counts
cnts = zeros(Int, m)
for i in 1:n
@inbounds si = samples[i]
if rmin <= si <= rmax
cnts[si - rmin + 1] += 1
else
vmin <= si <= vmax ||
error("Sample value out of valid range.")
end
end

# check the counts
for i in 1:m
verbose && println("v = $(rmin + i - 1) ==> ($(clb[i]), $(cub[i])): $(cnts[i])")
clb[i] <= cnts[i] <= cub[i] ||
error("The counts are out of the confidence interval.")
end
return samples
end

println("testing count random sampler")
for λ in [0.2, 0.5, 1.0, 2.0, 5.0, 10.0, 15.0, 20.0, 30.0]
test_samples(PoissonRandom.count_rand, Distributions.Poisson(λ), n_tsamples)
end

println("testing ad random sampler")
for λ in [5.0, 10.0, 15.0, 20.0, 30.0]
test_samples(PoissonRandom.ad_rand, Distributions.Poisson(λ), n_tsamples)
end

println("testing mixed random sampler")
for λ in [5.0, 10.0, 15.0, 20.0, 30.0]
test_samples(pois_rand, Distributions.Poisson(λ), n_tsamples)
end

@testset "BigFloat support" begin
@testset "count_rand with BigFloat (λ < 6)" begin
for _ in 1:100
result = pois_rand(BigFloat(3.0))
@test result isa Integer
@test result >= 0
end
end
@testset "ad_rand with BigFloat (λ >= 6)" begin
for _ in 1:100
result = pois_rand(BigFloat(15.0))
@test result isa Integer
@test result >= 0
end
end
@testset "statistical validity with BigFloat" begin
n = 10000
λ = BigFloat(10.0)
samples = [pois_rand(λ) for _ in 1:n]
sample_mean = mean(samples)
@test abs(sample_mean - Float64(λ)) < 3 * sqrt(Float64(λ))
end
end

@testset "PassthroughRNG dispatch" begin
using Random: Random, UInt52Raw
prng = PassthroughRNG()
# The CUDA.jl @device_override Random.randexp(::AbstractRNG) shadows our
# specific Random.randexp(::PassthroughRNG) on the GPU because Julia's
# OverlayMethodTable returns overlay matches without consulting the base
# table when the overlay fully covers the signature. The override body
# then calls these against PassthroughRNG; if they MethodError, kernel
# compilation fails with InvalidIRError on jl_f_throw_methoderror.
@test Random.rng_native_52(prng) === UInt64
@test Random.rand(prng, UInt52Raw()) isa UInt64
@test Random.rand(prng, UInt64) isa UInt64
@test Random.rand(prng, Float32) isa Float32
@test Random.rand(prng, Float64) isa Float64
end

@testset "Allocation Tests" begin
include("alloc_tests.jl")
end
end
using SciMLTesting
run_tests()
91 changes: 91 additions & 0 deletions test/sampler_tests.jl
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using PoissonRandom
import Distributions
using Statistics

n_tsamples = 10^5

function test_samples(
rand_func,
distr::Distributions.DiscreteUnivariateDistribution,
n::Int; # number of samples to generate
q::Float64 = 1.0e-8, # confidence interval, 1 - q as confidence
verbose::Bool = false
) # show intermediate info (for debugging)

# The basic idea
# ------------------
# Generate n samples, and count the occurrences of each value within a reasonable range.
# For each distinct value, it computes an confidence interval of the counts
# and checks whether the count is within this interval.
#
# If the distribution has a bounded range, it also checks whether
# the samples are all within this range.
#
# By setting a small q, we ensure that failure of the tests rarely
# happen in practice.
#
λ = distr.λ
n > 1 || error("The number of samples must be greater than 1.")
0.0 < q < 0.1 || error("The value of q must be within the open interval (0.0, 0.1).")

# determine the range of values to examine
vmin = minimum(distr)
vmax = maximum(distr)

rmin = floor(Int, quantile(distr, 0.00001))::Int
rmax = floor(Int, quantile(distr, 0.99999))::Int
m = rmax - rmin + 1 # length of the range
p0 = Distributions.pdf.((distr,), rmin:rmax) # reference probability masses
@assert length(p0) == m

# determine confidence intervals for counts:
# with probability q, the count will be out of this interval.
#
clb = Vector{Int}(undef, m)
cub = Vector{Int}(undef, m)
for i in 1:m
bp = Distributions.Binomial(n, p0[i])
clb[i] = floor(Int, quantile(bp, q / 2))
cub[i] = ceil(Int, Distributions.cquantile(bp, q / 2))
@assert cub[i] >= clb[i]
end

# generate samples
samples = [rand_func(λ) for i in 1:n]
@assert length(samples) == n

# scan samples and get counts
cnts = zeros(Int, m)
for i in 1:n
@inbounds si = samples[i]
if rmin <= si <= rmax
cnts[si - rmin + 1] += 1
else
vmin <= si <= vmax ||
error("Sample value out of valid range.")
end
end

# check the counts
for i in 1:m
verbose && println("v = $(rmin + i - 1) ==> ($(clb[i]), $(cub[i])): $(cnts[i])")
clb[i] <= cnts[i] <= cub[i] ||
error("The counts are out of the confidence interval.")
end
return samples
end

println("testing count random sampler")
for λ in [0.2, 0.5, 1.0, 2.0, 5.0, 10.0, 15.0, 20.0, 30.0]
test_samples(PoissonRandom.count_rand, Distributions.Poisson(λ), n_tsamples)
end

println("testing ad random sampler")
for λ in [5.0, 10.0, 15.0, 20.0, 30.0]
test_samples(PoissonRandom.ad_rand, Distributions.Poisson(λ), n_tsamples)
end

println("testing mixed random sampler")
for λ in [5.0, 10.0, 15.0, 20.0, 30.0]
test_samples(pois_rand, Distributions.Poisson(λ), n_tsamples)
end
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