Merge pull request #89 from tpapp/tp/remove-transformvariables

Remove TransformVariables.

Author: tpapp

.github/workflows/CI.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.0' # Replace this with the minimum Julia version that your package supports. E.g. if your package requires Julia 1.5 or higher, change this to '1.5'.
+          - '1.6' # Replace this with the minimum Julia version that your package supports. E.g. if your package requires Julia 1.5 or higher, change this to '1.5'.
           - '1' # Leave this line unchanged. '1' will automatically expand to the latest stable 1.x release of Julia.
           # - 'nightly' # NOTE: nightly disabled as it currently fails
         os:

Project.toml

@@ -1,15 +1,14 @@
 name = "LogDensityProblems"
 uuid = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 authors = ["Tamas K. Papp <[email protected]>"]
-version = "0.12.0"
+version = "1.0.0"
 
 [deps]
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
-TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 
 [compat]
@@ -18,9 +17,8 @@ BenchmarkTools = "1"
 DiffResults = "0.0, 1"
 DocStringExtensions = "0.8, 0.9"
 Requires = "0.5, 1"
-TransformVariables = "0.2, 0.3, 0.4, 0.5, 0.6"
 UnPack = "0.1, 1"
-julia = "1"
+julia = "1.6"
 
 [extras]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"

README.md

@@ -13,8 +13,12 @@ A common framework for implementing and using log densities for inference, provi
 
 2. The [`ADgradient`](https://tamaspapp.eu/LogDensityProblems.jl/dev/#LogDensityProblems.ADgradient) which makes objects that support `logdensity` to calculate log density *values* calculate log density *gradients* using various automatic differentiation packages.
 
-3. The wrapper [`TransformedLogDensity`](https://tamaspapp.eu/LogDensityProblems.jl/dev/#LogDensityProblems.TransformedLogDensity) using the [TransformVariables.jl](https://github.com/tpapp/TransformVariables.jl) package, allowing callables that take a set of parameters transformed from a flat vector of real numbers to support the `logdensity` interface.
+3. Various utility functions for debugging and testing log densities.
 
-4. Various utility functions for debugging and testing log densities.
+**NOTE** As of version 1.0, transformed log densities have been moved to [TransformedLogDensities.jl](https://github.com/tpapp/TransformedLogDensities.jl). Existing code that uses `TransformedLogDensity` should add
+```
+using TransformedLogDensities
+```
+or equivalent.
 
 See the [documentation](https://tpapp.github.io/LogDensityProblems.jl/dev) for details.

docs/Project.toml

@@ -4,6 +4,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
+TransformedLogDensities = "f9bc47f6-f3f8-4f3b-ab21-f8bc73906f26"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 

docs/make.jl

@@ -1,4 +1,4 @@
-using Documenter, LogDensityProblems, ForwardDiff, Tracker, Zygote, BenchmarkTools
+using Documenter, LogDensityProblems, ForwardDiff, Tracker, Zygote, BenchmarkTools, TransformedLogDensities
 
 makedocs(
     sitename = "LogDensityProblems.jl",

docs/src/index.md

@@ -87,8 +87,11 @@ problem((μ = 0.0, σ = 1.0))
 
 In our example, we require ``\sigma > 0``, otherwise the problem is meaningless. However, many MCMC samplers prefer to operate on *unconstrained* spaces ``\mathbb{R}^n``. The TransformVariables package was written to transform unconstrained to constrained spaces, and help with the log Jacobian correction (more on that later). That package has detailed documentation, now we just define a transformation from a length 2 vector to a `NamedTuple` with fields `μ` (unconstrained) and `σ > 0`.
 
+!!! note
+    Since version 1.0, TransformedLogDensity has been moved to the package TransformedLogDensities.
+
 ```@repl 1
-using LogDensityProblems, TransformVariables
+using LogDensityProblems, TransformVariables, TransformedLogDensities
 ℓ = TransformedLogDensity(as((μ = asℝ, σ = asℝ₊)), problem)
 ```
 
@@ -101,13 +104,9 @@ LogDensityProblems.logdensity(ℓ, zeros(2))
 !!! note
     Before running time-consuming algorithms like MCMC, it is advisable to test and benchmark your log density evaluations separately. The same applies to [`LogDensityProblems.logdensity_and_gradient`](@ref).
 
-```@docs
-TransformedLogDensity
-```
-
 ## Manual unpacking and transformation
 
-If you prefer to implement the transformation yourself, you just have to define the following three methods for your problem: declare that it can evaluate log densities (but not their gradient, hence the `0` order), allow the dimension of the problem to be queried, and then finally code the density calculation with the transformation. (Note that using [`TransformedLogDensity`](@ref) takes care of all of these for you, as shown above).
+If you prefer to implement the transformation yourself, you just have to define the following three methods for your problem: declare that it can evaluate log densities (but not their gradient, hence the `0` order), allow the dimension of the problem to be queried, and then finally code the density calculation with the transformation. (Note that using `TransformedLogDensities.TransformedLogDensity` takes care of all of these for you, as shown above).
 
 ```@example 1
 function LogDensityProblems.capabilities(::Type{<:NormalPosterior})

src/LogDensityProblems.jl

@@ -12,14 +12,13 @@ documentation.
 """
 module LogDensityProblems
 
-export TransformedLogDensity, ADgradient
+export ADgradient
 
 using ArgCheck: @argcheck
 using DocStringExtensions: SIGNATURES, TYPEDEF
-using UnPack: @unpack
-import Random
+using Random: AbstractRNG, default_rng
 using Requires: @require
-import TransformVariables
+using UnPack: @unpack
 
 ####
 #### interface for problems
@@ -128,50 +127,6 @@ The first argument (the log density) can be shifted by a constant, see the note
 """
 function logdensity_and_gradient end
 
-#####
-##### Transformed log density (typically Bayesian inference)
-#####
-
-"""
-    TransformedLogDensity(transformation, log_density_function)
-
-A problem in Bayesian inference. Vectors of length compatible with the dimension (obtained
-from `transformation`) are transformed into a general object `θ` (unrestricted type, but a
-named tuple is recommended for clean code), correcting for the log Jacobian determinant of
-the transformation.
-
-`log_density_function(θ)` is expected to return *real numbers*. For zero densities or
-infeasible `θ`s, `-Inf` or similar should be returned, but for efficiency of inference most
-methods recommend using `transformation` to avoid this. It is recommended that
-`log_density_function` is a callable object that also encapsulates the data for the problem.
-
-Use the property accessors `ℓ.transformation` and `ℓ.log_density_function` to access the
-arguments of `ℓ::TransformedLogDensity`, these are part of the public API.
-
-# Usage note
-
-This is the most convenient way to define log densities, as `capabilities`, `logdensity`,
-and `dimension` are automatically defined. To obtain a type that supports derivatives, use
-[`ADgradient`](@ref).
-"""
-struct TransformedLogDensity{T <: TransformVariables.AbstractTransform, L}
-    transformation::T
-    log_density_function::L
-end
-
-function Base.show(io::IO, ℓ::TransformedLogDensity)
-    print(io, "TransformedLogDensity of dimension $(dimension(ℓ))")
-end
-
-capabilities(::Type{<:TransformedLogDensity}) = LogDensityOrder{0}()
-
-dimension(p::TransformedLogDensity) = TransformVariables.dimension(p.transformation)
-
-function logdensity(p::TransformedLogDensity, x::AbstractVector)
-    @unpack transformation, log_density_function = p
-    TransformVariables.transform_logdensity(transformation, log_density_function, x)
-end
-
 #####
 ##### AD wrappers --- interface and generic code
 #####
@@ -244,38 +199,8 @@ function __init__()
     @require Enzyme="7da242da-08ed-463a-9acd-ee780be4f1d9" include("AD_Enzyme.jl")
 end
 
-####
-#### stress testing
-####
-
-"""
-$(SIGNATURES)
-
-Test `ℓ` with random values.
-
-`N` random vectors are drawn from a standard multivariate Cauchy distribution, scaled with
-`scale` (which can be a scalar or a conformable vector).
-
-Each random vector is then used as an argument in `f(ℓ, ...)`. `logdensity` and
-`logdensity_and_gradient` are  recommended for `f`.
-
-In case the call produces an error, the value is recorded as a failure, which are returned
-by the function.
+include("utilities.jl")
 
-Not exported, but part of the API.
-"""
-function stresstest(f, ℓ; N = 1000, rng::Random.AbstractRNG = Random.GLOBAL_RNG, scale = 1)
-    failures = Vector{Float64}[]
-    d = dimension(ℓ)
-    for _ in 1:N
-        x = TransformVariables.random_reals(d; scale = scale, cauchy = true, rng = rng)
-        try
-            f(ℓ, x)
-        catch e
-            push!(failures, x)
-        end
-    end
-    failures
-end
+Base.@deprecate_moved TransformedLogDensity "TransformedLogDensities"
 
 end # module

src/utilities.jl

@@ -0,0 +1,60 @@
+#####
+##### utilities
+#####
+
+####
+#### random reals
+####
+
+function _random_reals_scale(rng::AbstractRNG, scale::Real, cauchy::Bool)
+    cauchy ? scale / abs2(randn(rng)) : scale * 1.0
+end
+
+"""
+$(SIGNATURES)
+
+Random vector in ``ℝⁿ`` of length `n`.
+
+A standard multivaritate normal or Cauchy is used, depending on `cauchy`, then scaled with
+`scale`. `rng` is the random number generator used.
+
+Not exported, but part of the API.
+"""
+function random_reals(n::Integer; scale::Real = 1, cauchy::Bool = false,
+                      rng::AbstractRNG = default_rng())
+    randn(rng, n) .* _random_reals_scale(rng, scale, cauchy)
+end
+
+####
+#### stress testing
+####
+
+"""
+$(SIGNATURES)
+
+Test `ℓ` with random values.
+
+`N` random vectors are drawn from a standard multivariate Cauchy distribution, scaled with
+`scale` (which can be a scalar or a conformable vector).
+
+Each random vector is then used as an argument in `f(ℓ, ...)`. `logdensity` and
+`logdensity_and_gradient` are  recommended for `f`.
+
+In case the call produces an error, the value is recorded as a failure, which are returned
+by the function.
+
+Not exported, but part of the API.
+"""
+function stresstest(f, ℓ; N = 1000, rng::AbstractRNG = default_rng(), scale = 1)
+    failures = Vector{Float64}[]
+    d = dimension(ℓ)
+    for _ in 1:N
+        x = random_reals(d; scale = scale, cauchy = true, rng = rng)
+        try
+            f(ℓ, x)
+        catch e
+            push!(failures, x)
+        end
+    end
+    failures
+end

test/runtests.jl

@@ -9,11 +9,11 @@
     struct EnzymeTestMode <: Enzyme.Mode end
 end
 
-using LogDensityProblems, Test, Distributions, TransformVariables, BenchmarkTools
+using LogDensityProblems, Test, Distributions, BenchmarkTools
 import LogDensityProblems: capabilities, dimension, logdensity
 using LogDensityProblems: logdensity_and_gradient, LogDensityOrder
 
-import ForwardDiff, Tracker, TransformVariables, Random, Zygote, ReverseDiff
+import ForwardDiff, Tracker, Random, Zygote, ReverseDiff
 using UnPack: @unpack
 
 ####
@@ -65,7 +65,7 @@ end
 end
 
 ###
-### simple log density for testing
+### simple log densities for testing
 ###
 
 struct TestLogDensity{F}
@@ -79,6 +79,10 @@ test_gradient(x) = x .* [-4, -6, -10]
 TestLogDensity() = TestLogDensity(test_logdensity) # default: -Inf for negative input
 Base.show(io::IO, ::TestLogDensity) = print(io, "TestLogDensity")
 
+struct TestLogDensity2 end
+logdensity(::TestLogDensity2, x) = -sum(abs2, x)
+dimension(::TestLogDensity2) = 20
+
 ####
 #### traits
 ####
@@ -153,13 +157,6 @@ end
     @test LogDensityProblems.heuristic_chunks(82) == vcat(1:4:81, [82])
 end
 
-@testset "benchmark ForwardDiff chunk size" begin
-    ℓ = TransformedLogDensity(as(Array, 20), x -> -sum(abs2, x))
-    b = LogDensityProblems.benchmark_ForwardDiff_chunks(ℓ)
-    @test b isa Vector{Pair{Int,Float64}}
-    @test length(b) ≤ 20
-end
-
 @testset "AD via Tracker" begin
     ℓ = TestLogDensity()
     ∇ℓ = ADgradient(:Tracker, ℓ)
@@ -221,65 +218,13 @@ end
 
 @testset "ADgradient missing method" begin
     msg = "Don't know how to AD with Foo, consider `import Foo` if there is such a package."
-    P = TransformedLogDensity(as(Array, 1), x -> sum(abs2, x))
-    @test_logs((:info, msg), @test_throws(MethodError, ADgradient(:Foo, P)))
-end
-
-####
-#### transformed Bayesian problem
-####
-
-@testset "transformed Bayesian problem" begin
-    t = as((y = asℝ₊, ))
-    d = LogNormal(1.0, 2.0)
-    logposterior = ((x, ), ) -> logpdf(d, x)
-
-    # a Bayesian problem
-    p = TransformedLogDensity(t, logposterior)
-    @test repr(p) == "TransformedLogDensity of dimension 1"
-    @test dimension(p) == 1
-    @test p.transformation ≡ t
-    @test capabilities(p) == LogDensityOrder(0)
-
-    # gradient of a problem
-    ∇p = ADgradient(:ForwardDiff, p)
-    @test dimension(∇p) == 1
-    @test parent(∇p).transformation ≡ t
-
-    for _ in 1:100
-        x = random_arg(t)
-        θ, lj = transform_and_logjac(t, x)
-        px = logdensity(p, x)
-        @test logpdf(d, θ.y) + lj ≈ (px::Real)
-        px2, ∇px = logdensity_and_gradient(∇p, x)
-        @test px2 == px
-        @test ∇px ≈ [ForwardDiff.derivative(x -> logpdf(d, exp(x)) + x, x[1])]
-    end
+    @test_logs((:info, msg), @test_throws(MethodError, ADgradient(:Foo, TestLogDensity2())))
 end
 
-@testset "-∞ log densities" begin
-    t = as(Array, 2)
-    validx = x -> all(x .> 0)
-    p = TransformedLogDensity(t, x -> validx(x) ?  sum(abs2, x)/2 : -Inf)
-    ∇p = ADgradient(:ForwardDiff, p)
-
-    @test dimension(p) == dimension(∇p) == TransformVariables.dimension(t)
-    @test p.transformation ≡ parent(∇p).transformation ≡ t
-
-    for _ in 1:100
-        x = random_arg(t)
-        px = logdensity(∇p, x)
-        px_∇px = logdensity_and_gradient(∇p, x)
-        @test px isa Real
-        @test px_∇px isa Tuple{Real,Any}
-        @test first(px_∇px) ≡ px
-        if validx(x)
-            @test px ≈ sum(abs2, x)/2
-            @test last(px_∇px) ≈ x
-        else
-            @test isinf(px)
-        end
-    end
+@testset "benchmark ForwardDiff chunk size" begin
+    b = LogDensityProblems.benchmark_ForwardDiff_chunks(TestLogDensity2())
+    @test b isa Vector{Pair{Int,Float64}}
+    @test length(b) ≤ 20
 end
 
 @testset "stresstest" begin