Support DensityInterface API

Project.toml

@@ -1,10 +1,11 @@
 name = "Distributions"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
 authors = ["JuliaStats"]
-version = "0.25.24"
+version = "0.25.25"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
@@ -19,6 +20,7 @@ StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 
 [compat]
 ChainRulesCore = "1"
+DensityInterface = "0.3.2"
 FillArrays = "0.9, 0.10, 0.11, 0.12"
 PDMats = "0.10, 0.11"
 QuadGK = "2"

docs/make.jl

@@ -17,6 +17,7 @@ makedocs(
  "mixture.md",
  "fit.md",
  "extends.md",
+ "density_interface.md",
  ]
 )
 

docs/src/density_interface.md

@@ -0,0 +1,5 @@
+# Support for DensityInterface
+
+`Distributions` supports [`DensityInterface`](https://github.com/JuliaMath/DensityInterface.jl) for distributions.
+
+For *single* variate values `x`, `DensityInterface.logdensityof(d::Distribution, x)` is equivalent to `logpdf(d, x)` and `DensityInterface.densityof(d::Distribution, x)` is equivalent to `pdf(d, x)`.

src/Distributions.jl

@@ -27,6 +27,8 @@ using SpecialFunctions
 
 import ChainRulesCore
 
+import DensityInterface
+
 export
  # re-export Statistics
  mean, median, quantile, std, var, cov, cor,
@@ -299,6 +301,9 @@ include("pdfnorm.jl")
 include("mixtures/mixturemodel.jl")
 include("mixtures/unigmm.jl")
 
+# Implementation of DensityInterface API
+include("density_interface.jl")
+
 include("deprecates.jl")
 
 """

src/density_interface.jl

@@ -0,0 +1,19 @@
+@inline DensityInterface.hasdensity(::Distribution) = true
+
+for (di_func, d_func) in ((:logdensityof, :logpdf), (:densityof, :pdf))
+ @eval begin
+ DensityInterface.$di_func(d::Distribution, x) = $d_func(d, x)
+
+ function DensityInterface.$di_func(d::UnivariateDistribution, x::AbstractArray)
+ throw(ArgumentError("$(DensityInterface.$di_func) doesn't support multiple samples as an argument"))
+ end
+
+ function DensityInterface.$di_func(d::MultivariateDistribution, x::AbstractMatrix)
+ throw(ArgumentError("$(DensityInterface.$di_func) doesn't support multiple samples as an argument"))
+ end
+
+ function DensityInterface.$di_func(d::MatrixDistribution, x::AbstractArray{<:AbstractMatrix{<:Real}})
+ throw(ArgumentError("$(DensityInterface.$di_func) doesn't support multiple samples as an argument"))
+ end
+ end
+end

test/density_interface.jl

@@ -0,0 +1,25 @@
+@testset "DensityInterface" begin
+ using DensityInterface
+
+ d_uv_continous = Normal(-1.5, 2.3)
+ d_uv_discrete = Poisson(4.7)
+ d_mv = MvNormal([2.3 0.4; 0.4 1.2])
+ d_av = Distributions.MatrixReshaped(MvNormal(rand(10)), 2, 5)
+
+ @testset "Distribution" begin
+ for d in (d_uv_continous, d_uv_discrete, d_mv, d_av)
+ x = rand(d)
+ ref_logd_at_x = logpdf(d, x)
+ DensityInterface.test_density_interface(d, x, ref_logd_at_x)
+
+ # Stricter than required by test_density_interface:
+ @test logfuncdensity(logdensityof(d)) === d
+ end
+
+ for di_func in (logdensityof, densityof)
+ @test_throws ArgumentError di_func(d_uv_continous, [rand(d_uv_continous) for i in 1:3])
+ @test_throws ArgumentError di_func(d_mv, hcat([rand(d_mv) for i in 1:3]...))
+ @test_throws ArgumentError di_func(d_av, [rand(d_av) for i in 1:3])
+ end
+ end
+end

test/runtests.jl

@@ -63,7 +63,8 @@ const tests = [
  "gumbel",
  "pdfnorm",
  "rician",
- "functionals", 
+ "functionals",
+ "density_interface",
 ]
 
 printstyled("Running tests:\n", color=:blue)