Renamed logpdf(gp, y) to _logpdf(gp, y) to respect Distributions.jl A…

…PI and add Turing compatibility test (#52) * Renamed logpdf(gp, y) to _logpdf(gp, y) to resepect Distributions.jl API * Limit _logpdf to vectors and add specific methods for matrices * Correct behaviour for `loglikelihood` * Making product on y a scalar and removing _logpdf! * Returned docs and restored definition of logpdf for _logpdf * Missing docs * Correct typo on docs Co-authored-by: David Widmann <[email protected]> * Removing linebreak Co-authored-by: willtebbutt <[email protected]> * Adding tests for loglikelihood * Added a simple test with a Turing model * Adding Turing to test/Project.toml * Fixing Turing test * Modified syntax Turing test and changed test_nowarn to test * Added a test for the presence of docs * Bump patch version Co-authored-by: David Widmann <[email protected]> Co-authored-by: willtebbutt <[email protected]>
JuliaGaussianProcesses · Sep 7, 2020 · ee483a4 · ee483a4 · theogf · Sep 7, 2020
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diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,8 @@
 name = "AbstractGPs"
 uuid = "99985d1d-32ba-4be9-9821-2ec096f28918"
 authors = ["willtebbutt <[email protected]>"]
-version = "0.2.7"
+version = "0.2.8"
+
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

diff --git a/src/abstract_gp/finite_gp.jl b/src/abstract_gp/finite_gp.jl
@@ -213,10 +213,14 @@ julia> logpdf(f(x), Y) isa AbstractVector{<:Real}
 true
 ```
 """
-function Distributions.logpdf(f::FiniteGP, y::AbstractVector{<:Real})
+logpdf(f::FiniteGP, y::AbstractVecOrMat{<:Real})
+
+function Distributions._logpdf(f::FiniteGP, y::AbstractVector{<:Real})
     return first(logpdf(f, reshape(y, :, 1)))
 end
 
+Distributions.loglikelihood(f::FiniteGP, Y::AbstractMatrix{<:Real}) = sum(logpdf(f, Y))
+
 function Distributions.logpdf(f::FiniteGP, Y::AbstractMatrix{<:Real})
     m, C_mat = mean_and_cov(f)
     C = cholesky(Symmetric(C_mat))

diff --git a/test/Project.toml b/test/Project.toml
@@ -8,6 +8,7 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
@@ -16,5 +17,6 @@ Distributions = "0.19, 0.20, 0.21, 0.22, 0.23"
 Documenter = "0.24, 0.25"
 FiniteDifferences = "0.9.6, 0.10"
 Plots = "1"
+Turing = "0.14"
 Zygote = "0.5"
 julia = "1.3"
diff --git a/test/abstract_gp/finite_gp.jl b/test/abstract_gp/finite_gp.jl
@@ -109,7 +109,7 @@ end
     #         )
     #     end
     # end
-    @testset "logpdf / elbo / dtc" begin
+    @testset "logpdf / loglikelihood / elbo / dtc" begin
         rng = MersenneTwister(123456)
         N = 10
         S = 11
@@ -123,12 +123,13 @@ end
         # Check that logpdf returns the correct type and roughly agrees with Distributions.
         @test logpdf(y, ŷ) isa Real
         @test logpdf(y, ŷ) ≈ logpdf(MvNormal(Vector(mean(y)), cov(y)), ŷ)
-
+        @test loglikelihood(y, ŷ) == logpdf(y, ŷ)
         # Check that multi-sample logpdf returns the correct type and is consistent with
         # single-sample logpdf
         Ŷ = rand(rng, y, S)
         @test logpdf(y, Ŷ) isa Vector{Float64}
         @test logpdf(y, Ŷ) ≈ [logpdf(y, Ŷ[:, n]) for n in 1:S]
+        @test loglikelihood(y, Ŷ) == sum(logpdf(y, Ŷ))
 
         # # Check gradient of logpdf at mean is zero for `f`.
         # adjoint_test(ŷ->logpdf(fx, ŷ), 1, ones(size(ŷ)))
@@ -207,6 +208,11 @@ end
     end
 end
 
+    @testset "Docs" begin
+        docstring = string(Docs.doc(logpdf, Tuple{FiniteGP, Vector{Float64}}))
+        @test contains(docstring, "logpdf(f::FiniteGP, y::AbstractVecOrMat{<:Real})")
+    end
+
 # """
 #     simple_gp_tests(rng::AbstractRNG, f::GP, xs::AV{<:AV}, σs::AV{<:Real})
 

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -1,6 +1,6 @@
 using AbstractGPs
-using AbstractGPs: AbstractGP, MeanFunction, FiniteGP, ConstMean, GP, ZeroMean, 
-    ConstMean, CustomMean, Xt_A_X, Xt_A_Y, Xt_invA_Y, Xt_invA_X, diag_At_A, diag_At_B, 
+using AbstractGPs: AbstractGP, MeanFunction, FiniteGP, ConstMean, GP, ZeroMean,
+    ConstMean, CustomMean, Xt_A_X, Xt_A_Y, Xt_invA_Y, Xt_invA_X, diag_At_A, diag_At_B,
     diag_Xt_A_X, diag_Xt_A_Y, diag_Xt_invA_X, diag_Xt_invA_Y, Xtinv_A_Xinv, tr_At_A,
     mean_and_cov_diag
 
@@ -15,6 +15,7 @@ using Plots
 using Random
 using Statistics
 using Test
+using Turing
 using Zygote
 
 
@@ -40,6 +41,8 @@ include("test_util.jl")
     include(joinpath("latent_gp", "latent_gp.jl"))
 
     include(joinpath("util", "plotting.jl"))
+
+    include("turing.jl")
 
     @testset "doctests" begin
         DocMeta.setdocmeta!(

diff --git a/test/turing.jl b/test/turing.jl
@@ -0,0 +1,25 @@
+@testset "turing compat" begin
+    k = SqExponentialKernel()
+    y = randn(3)
+    X = randn(3, 1)
+    x = [rand(1) for _ in 1:3]
+    @model function GPRegression(y, X)
+        # Priors.
+        α ~ LogNormal(0.0, 0.1)
+        ρ ~ LogNormal(0.0, 1.0)
+        σ² ~ LogNormal(0.0, 1.0)
+
+        # Realized covariance function
+        kernel = α * transform(SqExponentialKernel(), 1 / ρ)
+        f = GP(kernel)
+
+        # Sampling Distribution.
+        y ~ f(X, σ²)
+    end
+    # Test for matrices
+    m = GPRegression(y, RowVecs(X))
+    @test length(sample(m, HMC(0.5, 1), 5)) == 5
+    # Test for vectors of vector
+    m = GPRegression(y, x)
+    @test length(sample(m, HMC(0.5, 1), 5)) == 5
+end