Merge pull request #67 from JuliaAI/dev

For a 0.1.12 release

.github/workflows/ci.yml

@@ -19,7 +19,6 @@ jobs:
         version:
           - '1.6'
           - '1' # automatically expands to the latest stable 1.x release of Julia.
-          - '~1.9.0-0'
         os:
           - ubuntu-latest
         arch:

Project.toml

@@ -1,7 +1,7 @@
 name = "CategoricalDistributions"
 uuid = "af321ab8-2d2e-40a6-b165-3d674595d28e"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.11"
+version = "0.1.12"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -19,6 +19,7 @@ UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
 UnivariateFiniteDisplayExt = "UnicodePlots"
 
 [compat]
+BenchmarkTools = "1.3.2"
 CategoricalArrays = "0.9, 0.10"
 Distributions = "0.25"
 Missings = "0.4, 1"
@@ -28,11 +29,12 @@ UnicodePlots = "2, 3"
 julia = "1.6"
 
 [extras]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
 
 [targets]
-test = ["FillArrays", "Random", "StableRNGs", "Test", "UnicodePlots"]
+test = ["BenchmarkTools", "FillArrays", "Random", "StableRNGs", "Test", "UnicodePlots"]

src/methods.jl

@@ -215,6 +215,8 @@ end
 """
     _cumulative(d::UnivariateFinite)
 
+**Private method.**
+
 Return the cumulative probability vector `C` for the distribution `d`,
 using only classes in the support of `d`, ordered according to the
 categorical elements used at instantiation of `d`. Used only to
@@ -238,6 +240,8 @@ end
 """
 _rand(rng, p_cumulative, R)
 
+**Private method.**
+
 Randomly sample the distribution with discrete support `R(1):R(n)`
 which has cumulative probability vector `p_cumulative` (see
 [`_cummulative`](@ref)).
@@ -256,26 +260,27 @@ function _rand(rng, p_cumulative, R)
     return index
 end
 
-function Base.rand(rng::AbstractRNG,
-                   d::UnivariateFinite{<:Any,<:Any,R}) where R
-    p_cumulative = _cumulative(d)
-    return Dist.support(d)[_rand(rng, p_cumulative, R)]
+Random.eltype(::Type{<:UnivariateFinite{<:Any,V}}) where V = V
+
+# The Sampler hook into Random's API is discussed in the Julia documentation, in the
+# Standard Library section on Random.
+function Random.Sampler(
+    ::AbstractRNG,
+    d::UnivariateFinite,
+    ::Random.Repetition,
+    )
+    data = (_cumulative(d), Dist.support(d))
+    Random.SamplerSimple(d, data)
 end
 
-function Base.rand(rng::AbstractRNG,
-                   d::UnivariateFinite{<:Any,<:Any,R},
-                   dim1::Int, moredims::Int...) where R # ref type
-    p_cumulative = _cumulative(d)
-    A = Array{R}(undef, dim1, moredims...)
-    for i in eachindex(A)
-        @inbounds A[i] = _rand(rng, p_cumulative, R)
-    end
-    support = Dist.support(d)
-    return broadcast(i -> support[i], A)
+function Base.rand(
+    rng::AbstractRNG,
+    sampler::Random.SamplerSimple{<:UnivariateFinite{<:Any,<:Any,R}},
+    ) where R
+    p_cumulative, support = sampler.data
+    return support[_rand(rng, p_cumulative, R)]
 end
 
-rng(d::UnivariateFinite, args...) = rng(Random.GLOBAL_RNG, d, args...)
-
 function Dist.fit(d::Type{<:UnivariateFinite},
                            v::AbstractVector{C}) where C
     C <: CategoricalValue ||

test/arithmetic.jl

@@ -1,10 +1,15 @@
 module TestArithmetic
 
 using Test
+import BenchmarkTools: @belapsed
 using CategoricalDistributions
 using StableRNGs
 rng = StableRNG(123)
 
+macro belapsed1(ex)
+    :(@belapsed $ex seconds=1 evals=1)
+end
+
 L = ["yes", "no"]
 d1 = UnivariateFinite(L, rand(rng, 2), pool=missing)
 d2 = UnivariateFinite(L, rand(rng, 2), pool=missing)
@@ -46,27 +51,27 @@ fast = UnivariateFinite(L, P, pool=missing);
 
 @testset "performant arithmetic for UnivariateFiniteArray" begin
     @test pdf(slow + slow, L) == pdf(fast + fast, L)
-    t_slow = @elapsed @eval slow + slow
-    t_fast = @elapsed @eval fast + fast
+    t_slow = @belapsed1 $slow + $slow
+    t_fast = @belapsed1 $fast + $fast
     @test t_slow/t_fast > 10
 
     @test pdf(slow - slow, L) == pdf(fast - fast, L)
-    t_slow = @elapsed @eval slow - slow
-    t_fast = @elapsed @eval fast - fast
+    t_slow = @belapsed1 $slow - $slow
+    t_fast = @belapsed1 $fast - $fast
     @test t_slow/t_fast > 10
 
     @test pdf(42*slow, L) == pdf(42*fast, L)
     @test pdf(slow*42, L) == pdf(fast*42, L)
-    t_slow = @elapsed @eval 42*slow
-    t_fast = @elapsed @eval 42*fast
+    t_slow = @belapsed1 42*$slow
+    t_fast = @belapsed1 42*$fast
     @test t_slow/t_fast > 10
-    t_slow = @elapsed @eval slow*42
-    t_fast = @elapsed @eval fast*42
+    t_slow = @belapsed1 $slow*42
+    t_fast = @belapsed1 $fast*42
     @test t_slow/t_fast > 10
 
     @test pdf(slow/42, L) == pdf(fast/42, L)
-    t_slow = @elapsed @eval slow/42
-    t_fast = @elapsed @eval fast/42
+    t_slow = @belapsed1 $slow/42
+    t_fast = @belapsed1 $fast/42
     @test t_slow/t_fast > 10
 end
 

test/methods.jl

@@ -8,6 +8,7 @@ using StableRNGs
 import Random
 rng = StableRNG(123)
 using ScientificTypes
+import Random.default_rng
 
 import CategoricalDistributions: classes, ERR_NAN_FOUND
 
@@ -127,7 +128,7 @@ end
 @testset "broadcasting pdf over single UnivariateFinite object" begin
     d = UnivariateFinite(["a", "b"], [0.1, 0.9], pool=missing);
     @test pdf.(d, ["a", "b"]) == [0.1, 0.9]
-end 
+end
 
 @testset "constructor arguments not categorical values" begin
     @test_throws ArgumentError UnivariateFinite(Dict('f'=>0.7, 'q'=>0.2))
@@ -299,6 +300,28 @@ end
     @test displays_okay([5 + 3im, 4 - 7im])
 end
 
+@testset "rand signatures" begin
+    d = UnivariateFinite(
+        ["maybe", "no", "yes"],
+        [0.5, 0.4, 0.1];
+        pool=missing,
+    )
+
+    # smoke test:
+    sampler = Random.Sampler(default_rng(), d, Val(1))
+    rand(default_rng(), sampler)
+
+    Random.seed!(123)
+    samples = [rand(default_rng(), d) for i in 1:30]
+    Random.seed!(123)
+    @test [rand(d) for i in 1:30] == samples
+
+    Random.seed!(123)
+    samples = rand(Random.default_rng(), d, 3, 5)
+    Random.seed!(123)
+    @test samples == rand(d, 3, 5)
+end
+
 end # module
 
 true