move expected loglik to GPLikelihoods (#123)

* move to GPLikelihoods 0.4; remove expected_loglik from here
* remove unnecessary internal function

Author: st--

Project.toml

@@ -1,7 +1,7 @@
 name = "ApproximateGPs"
 uuid = "298c2ebc-0411-48ad-af38-99e88101b606"
 authors = ["JuliaGaussianProcesses Team"]
-version = "0.3.4"
+version = "0.4.0"
 
 [deps]
 AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
@@ -29,7 +29,7 @@ Distributions = "0.25"
 FastGaussQuadrature = "0.4"
 FillArrays = "0.12, 0.13"
 ForwardDiff = "0.10"
-GPLikelihoods = "0.3"
+GPLikelihoods = "0.4"
 IrrationalConstants = "0.1"
 LogExpFunctions = "0.3"
 PDMats = "0.11"

docs/Project.toml

@@ -3,5 +3,5 @@ ApproximateGPs = "298c2ebc-0411-48ad-af38-99e88101b606"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 
 [compat]
-ApproximateGPs = "0.3"
+ApproximateGPs = "0.3,0.4"
 Documenter = "0.27"

src/ApproximateGPs.jl

@@ -13,8 +13,6 @@ include("utils.jl")
 include("SparseVariationalApproximationModule.jl")
 @reexport using .SparseVariationalApproximationModule:
     SparseVariationalApproximation, Centered, NonCentered
-@reexport using .SparseVariationalApproximationModule:
-    DefaultQuadrature, Analytic, GaussHermite, MonteCarlo
 
 include("LaplaceApproximationModule.jl")
 @reexport using .LaplaceApproximationModule: LaplaceApproximation

src/SparseVariationalApproximationModule.jl

@@ -4,14 +4,12 @@ using ..API
 
 export SparseVariationalApproximation, Centered, NonCentered
 
-using ..ApproximateGPs: _chol_cov, _cov
 using Distributions
 using LinearAlgebra
 using Statistics
 using StatsBase
 using FillArrays: Fill
 using PDMats: chol_lower
-using IrrationalConstants: sqrt2, invsqrtπ
 
 using AbstractGPs: AbstractGPs
 using AbstractGPs:
@@ -24,10 +22,8 @@ using AbstractGPs:
     marginals,
     At_A,
     diag_At_A
-using GPLikelihoods: GaussianLikelihood
-
-export DefaultQuadrature, Analytic, GaussHermite, MonteCarlo
-include("expected_loglik.jl")
+using GPLikelihoods: GaussianLikelihood, DefaultExpectationMethod, expected_loglikelihood
+using ..ApproximateGPs: _chol_cov, _cov
 
 @doc raw"""
     Centered()
@@ -289,22 +285,20 @@ end
         fx::FiniteGP,
         y::AbstractVector{<:Real};
         num_data=length(y),
-        quadrature=DefaultQuadrature(),
+        quadrature=GPLikelihoods.DefaultExpectationMethod(),
     )
 
 Compute the Evidence Lower BOund from [1] for the process `f = fx.f ==
 svgp.fz.f` where `y` are observations of `fx`, pseudo-inputs are given by `z =
 svgp.fz.x` and `q(u)` is a variational distribution over inducing points `u =
 f(z)`.
 
-`quadrature` selects which method is used to calculate the expected loglikelihood in
-the ELBO. The options are: `DefaultQuadrature()`, `Analytic()`, `GaussHermite()` and
-`MonteCarlo()`. For likelihoods with an analytic solution, `DefaultQuadrature()` uses this
-exact solution. If there is no such solution, `DefaultQuadrature()` either uses
-`GaussHermite()` or `MonteCarlo()`, depending on the likelihood.
+`quadrature` is passed on to `GPLikelihoods.expected_loglikelihood` and selects
+which method is used to calculate the expected loglikelihood in the ELBO. See
+`GPLikelihoods.expected_loglikelihood` for more details.
 
 N.B. the likelihood is assumed to be Gaussian with observation noise `fx.Σy`.
-Further, `fx.Σy` must be isotropic - i.e. `fx.Σy = α * I`.
+Further, `fx.Σy` must be isotropic - i.e. `fx.Σy = σ² * I`.
 
 [1] - Hensman, James, Alexander Matthews, and Zoubin Ghahramani. "Scalable
 variational Gaussian process classification." Artificial Intelligence and
@@ -315,10 +309,11 @@ function AbstractGPs.elbo(
     fx::FiniteGP{<:AbstractGP,<:AbstractVector,<:Diagonal{<:Real,<:Fill}},
     y::AbstractVector{<:Real};
     num_data=length(y),
-    quadrature=DefaultQuadrature(),
+    quadrature=DefaultExpectationMethod(),
 )
-    @assert sva.fz.f === fx.f
-    return _elbo(quadrature, sva, fx, y, GaussianLikelihood(fx.Σy[1]), num_data)
+    σ² = fx.Σy[1]
+    lik = GaussianLikelihood(σ²)
+    return elbo(sva, LatentFiniteGP(fx, lik), y; num_data, quadrature)
 end
 
 function AbstractGPs.elbo(
@@ -337,7 +332,7 @@ end
         lfx::LatentFiniteGP,
         y::AbstractVector;
         num_data=length(y),
-        quadrature=DefaultQuadrature(),
+        quadrature=GPLikelihoods.DefaultExpectationMethod(),
     )
 
 Compute the ELBO for a LatentGP with a possibly non-conjugate likelihood.
@@ -347,26 +342,17 @@ function AbstractGPs.elbo(
     lfx::LatentFiniteGP,
     y::AbstractVector;
     num_data=length(y),
-    quadrature=DefaultQuadrature(),
-)
-    @assert sva.fz.f === lfx.fx.f
-    return _elbo(quadrature, sva, lfx.fx, y, lfx.lik, num_data)
-end
-
-# Compute the common elements of the ELBO
-function _elbo(
-    quadrature::QuadratureMethod,
-    sva::SparseVariationalApproximation,
-    fx::FiniteGP,
-    y::AbstractVector,
-    lik,
-    num_data::Integer,
+    quadrature=DefaultExpectationMethod(),
 )
-    @assert sva.fz.f === fx.f
+    sva.fz.f === lfx.fx.f || throw(
+        ArgumentError(
+            "(Latent)FiniteGP prior is not consistent with SparseVariationalApproximation's",
+        ),
+    )
 
     f_post = posterior(sva)
-    q_f = marginals(f_post(fx.x))
-    variational_exp = expected_loglik(quadrature, y, q_f, lik)
+    q_f = marginals(f_post(lfx.fx.x))
+    variational_exp = expected_loglikelihood(quadrature, lfx.lik, q_f, y)
 
     n_batch = length(y)
     scale = num_data / n_batch

src/expected_loglik.jl

@@ -17,7 +17,7 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 AbstractGPs = "0.4, 0.5"
-ApproximateGPs = "0.3"
+ApproximateGPs = "0.4"
 ChainRulesCore = "1"
 ChainRulesTestUtils = "1.2.3"
 Distributions = "0.25"