Add rng field to KLIEP and LSIF

Project.toml

@@ -6,6 +6,7 @@ version = "0.4.3"
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"

src/DensityRatioEstimation.jl

@@ -8,6 +8,7 @@ using StatsBase
 using Statistics
 using LinearAlgebra
 using Parameters
+using Random
 
 # implement fit for estimators
 import StatsBase: fit

src/kliep.jl

@@ -21,9 +21,10 @@ Kullback-Leibler importance estimation procedure (KLIEP).
 
 * Júlio Hoffimann ([email protected])
 """
-@with_kw struct KLIEP{T} <: DensityRatioEstimator
+@with_kw struct KLIEP{T,RNG} <: DensityRatioEstimator
   σ::T=2.0
   b::Int=10
+  rng::RNG=Random.GLOBAL_RNG
 end
 
 default_optlib(dre::Type{<:KLIEP}) = OptimLib
@@ -49,7 +50,7 @@ end
 
 # constants involved in KLIEP optimization
 function _kliep_consts(x_nu, x_de, dre)
-  x_ba = sample(x_nu, min(length(x_nu), dre.b), replace=false)
+  x_ba = sample(dre.rng, x_nu, min(length(x_nu), dre.b), replace=false)
   K_nu = gaussian_gramian(x_nu, x_ba, σ=dre.σ)
   K_de = gaussian_gramian(x_de, x_ba, σ=dre.σ)
 

src/lsif.jl

@@ -22,10 +22,11 @@ Importance Estimation
 
 * Júlio Hoffimann ([email protected])
 """
-@with_kw struct LSIF{T} <: DensityRatioEstimator
+@with_kw struct LSIF{T,RNG} <: DensityRatioEstimator
   σ::T=2.0
   b::Int=10
   λ::T=0.001
+  rng::RNG=Random.GLOBAL_RNG
 end
 
 default_optlib(dre::Type{<:LSIF}) = OptimLib
@@ -50,7 +51,7 @@ function _densratiofunc(x_nu, x_de, dre::LSIF,
 end
 
 function _lsif_consts(x_nu, x_de, dre)
-  x_ba = sample(x_nu, min(length(x_nu), dre.b), replace=false)
+  x_ba = sample(dre.rng, x_nu, min(length(x_nu), dre.b), replace=false)
   K_nu = gaussian_gramian(x_nu, x_ba, σ=dre.σ)
   K_de = gaussian_gramian(x_de, x_ba, σ=dre.σ)
 

test/kliep.jl

@@ -5,8 +5,8 @@
     x_nu, x_de = rand(rng, d_nu, 1_000), rand(rng, d_de, 500)
 
     # estimated density ratio
-    σ, b = 1.0, 100
-    r̂ = densratio(x_nu, x_de, KLIEP(σ=σ, b=b), optlib=optlib)
+    kliep = KLIEP(σ=1.0, b=100, rng=rng)
+    r̂ = densratio(x_nu, x_de, kliep, optlib=optlib)
 
     # simplex constraints
     @test abs(mean(r̂) - 1) ≤ 1e-2

test/lsif.jl

@@ -5,7 +5,7 @@
     x_nu, x_de = rand(rng, d_nu, 1000), rand(rng, d_de, 500)
 
     # estimated density ratio
-    lsif = LSIF(σ=1.0, b=100)
+    lsif = LSIF(σ=1.0, b=100, rng=rng)
     r̂ = densratio(x_nu, x_de, lsif, optlib=optlib)
 
     # simplex constraints