Merge pull request #51 from tpapp/tp/dustoff

use new LogDensityProblem interface

Author: tpapp

.travis.yml

@@ -6,6 +6,7 @@ os:
 julia:
   - 1.0
   - 1.1
+  - 1.2
   - nightly
 matrix:
   allow_failures:

CHANGELOG.md

@@ -1,5 +1,7 @@
 # Unreleased
 
+- use new LogDensityProblems interface
+
 # 0.3.4
 
 - make `inverse(::ArrayTransform)` accept `AbstractArray`

Manifest.toml

@@ -23,9 +23,9 @@ version = "0.5.6"
 
 [[CSTParser]]
 deps = ["Tokenize"]
-git-tree-sha1 = "0ff80f68f55fcde2ed98d7b24d7abaf20727f3f8"
+git-tree-sha1 = "c69698c3d4a7255bc1b4bc2afc09f59db910243b"
 uuid = "00ebfdb7-1f24-5e51-bd34-a7502290713f"
-version = "0.6.1"
+version = "0.6.2"
 
 [[CommonSubexpressions]]
 deps = ["Test"]
@@ -183,9 +183,9 @@ deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[Tokenize]]
-git-tree-sha1 = "c8a8b00ae44a94950814ff77850470711a360225"
+git-tree-sha1 = "dfcdbbfb2d0370716c815cbd6f8a364efb6f42cf"
 uuid = "0796e94c-ce3b-5d07-9a54-7f471281c624"
-version = "0.5.5"
+version = "0.5.6"
 
 [[URIParser]]
 deps = ["Test", "Unicode"]

Project.toml

@@ -13,7 +13,7 @@ Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [compat]
-LogDensityProblems = "^0.8.3"
+LogDensityProblems = "^0.9.0"
 julia = "^1"
 
 [extras]

test/runtests.jl

@@ -1,17 +1,21 @@
 using DocStringExtensions, LinearAlgebra, LogDensityProblems, OffsetArrays, Parameters,
     Random, Test, TransformVariables, StaticArrays
 import Flux, ForwardDiff, ReverseDiff
-using LogDensityProblems: Value, ValueGradient
+using LogDensityProblems: logdensity, logdensity_and_gradient
 using TransformVariables:
     AbstractTransform, ScalarTransform, VectorTransform, ArrayTransform,
     unit_triangular_dimension, logistic, logistic_logjac, logit, inverse_and_logjac
 
-include("test_utilities.jl")
+include("utilities.jl")
 
 Random.seed!(1)
 
 const CIENV = get(ENV, "TRAVIS", "") == "true"  || get(ENV, "CI", "") == "true"
 
+####
+#### utilities
+####
+
 @testset "misc utilities" begin
     @test unit_triangular_dimension(1) == 0
     @test unit_triangular_dimension(2) == 1
@@ -34,6 +38,10 @@ end
     end
 end
 
+####
+#### scalar transformations correctness checks
+####
+
 @testset "scalar transformations consistency" begin
     for _ in 1:100
         a = randn() * 100
@@ -70,6 +78,10 @@ end
     @test as_unit_interval ≡ as𝕀
 end
 
+####
+#### special array transformation correctness checks
+####
+
 @testset "to unit vector" begin
     @testset "dimension checks" begin
         U = UnitVector(3)
@@ -114,6 +126,14 @@ end
     end
 end
 
+####
+#### aggregation
+####
+
+###
+### array correctness checks
+###
+
 @testset "to array scalar" begin
     dims = (3, 4, 5)
     t = as𝕀
@@ -156,6 +176,10 @@ end
     @test lj == 0
 end
 
+###
+### tuple correctness checks
+###
+
 @testset "to tuple" begin
     t1 = asℝ
     t2 = as𝕀
@@ -182,6 +206,10 @@ end
     @test lj2 ≈ ljacc
 end
 
+###
+### named tuple correctness checks
+###
+
 @testset "to named tuple" begin
     t1 = asℝ
     t2 = CorrCholeskyFactor(7)
@@ -224,6 +252,10 @@ end
     @test_skip inverse(za, []) == []
 end
 
+####
+#### log density correctness checks
+####
+
 @testset "transform logdensity: correctness" begin
     # the density is p(σ) = σ⁻³
     # let z = log(σ), so σ = exp(z)
@@ -246,6 +278,10 @@ end
     @test (@inferred transform_logdensity(t, f, z)) isa Float64
 end
 
+####
+#### custom transformations
+####
+
 @testset "custom transformation: triangle below diagonal in [0,1]²" begin
     tfun(y) = y[1], y[1]*y[2]   # triangle below diagonal in unit square
     t = CustomTransform(as(Array, as𝕀, 2), tfun, collect;)
@@ -304,6 +340,10 @@ end
     end
 end
 
+####
+#### AD compatibility tests
+####
+
 @testset "AD tests" begin
     t = as((μ = asℝ, σ = asℝ₊, β = asℝ₋, α = as(Real, 0.0, 1.0),
             u = UnitVector(3), L = CorrCholeskyFactor(4)))
@@ -313,33 +353,28 @@ end
     end
     P = TransformedLogDensity(t, f)
     x = zeros(dimension(t))
-    v = logdensity(Value, P, x)
+    v = logdensity(P, x)
+    g = ForwardDiff.gradient(x -> logdensity(P, x), x)
 
     # ForwardDiff
     P1 = ADgradient(:ForwardDiff, P)
-    @test v == logdensity(Value, P1, x)
-    g1 = @inferred logdensity(ValueGradient, P1, x)
-    @test g1.value == v.value
+    @test v == logdensity(P1, x)
+    v1, g1 = @inferred logdensity_and_gradient(P1, x)
+    @test v1 == v
+    @test g1 ≈ g
 
     # Flux # NOTE @inferred removed as it currently fails, cf
     # https://github.com/FluxML/Flux.jl/issues/497
     P2 = ADgradient(:Flux, P)
-    g2 = logdensity(ValueGradient, P2, x) #
-    @test g2.value == v.value
-    @test g2.gradient ≈ g1.gradient
-
-    # test element type calculations for Flux
-    t2 = CorrCholeskyFactor(4)
-    @test t2(Flux.param(ones(dimension(t2)))) isa UpperTriangular
-
-    t3 = UnitVector(3)
-    @test sum(abs2, t3(Flux.param(ones(dimension(t3))))) ≈ Flux.param(1.0)
+    v2, g2 = logdensity_and_gradient(P2, x)
+    @test v2 == v
+    @test g2 ≈ g
 
     # ReverseDiff
     P3 = ADgradient(:ReverseDiff, P)
-    g3 = @inferred logdensity(ValueGradient, P3, x)
-    @test g3.value == v.value
-    @test g3.gradient ≈ g1.gradient
+    v3, g3 = @inferred logdensity_and_gradient(P3, x)
+    @test v3 == v
+    @test g3 ≈ g
 end
 
 @testset "inverse_and_logjac" begin

test/test_utilities.jl test/utilities.jltest/test_utilities.jl renamed to test/utilities.jl

@@ -1,3 +1,8 @@
+"""
+$(SIGNATURES)
+
+Log jacobian abs determinant via automatic differentiation. For testing.
+"""
 AD_logjac(t::VectorTransform, x, vec_y) =
     logabsdet(ForwardDiff.jacobian(x -> vec_y(transform(t, x)), x))[1]