From 626309b8e2dacf82cc0ec98518b3849f3abc6556 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Galy-Fajou?= Date: Wed, 1 Dec 2021 18:13:00 +0100 Subject: [PATCH 1/8] First go at the augmentation tutorial --- examples/d-augmented-gp/Manifest.toml | 1272 +++++++++++++++++++++++++ examples/d-augmented-gp/Project.toml | 11 + examples/d-augmented-gp/script.jl | 218 +++++ 3 files changed, 1501 insertions(+) create mode 100644 examples/d-augmented-gp/Manifest.toml create mode 100644 examples/d-augmented-gp/Project.toml create mode 100644 examples/d-augmented-gp/script.jl diff --git a/examples/d-augmented-gp/Manifest.toml b/examples/d-augmented-gp/Manifest.toml new file mode 100644 index 00000000..32c10710 --- /dev/null +++ b/examples/d-augmented-gp/Manifest.toml @@ -0,0 +1,1272 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.7.0" +manifest_format = "2.0" + +[[deps.AbstractFFTs]] +deps = ["LinearAlgebra"] +git-tree-sha1 = 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"298c2ebc-0411-48ad-af38-99e88101b606" +AugmentedGaussianProcesses = "38eea1fd-7d7d-5162-9d08-f89d0f2e271e" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306" +Optim = "429524aa-4258-5aef-a3af-852621145aeb" +ParameterHandling = "2412ca09-6db7-441c-8e3a-88d5709968c5" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" diff --git a/examples/d-augmented-gp/script.jl b/examples/d-augmented-gp/script.jl new file mode 100644 index 00000000..bbe5afba --- /dev/null +++ b/examples/d-augmented-gp/script.jl @@ -0,0 +1,218 @@ +# # Augmented Sparse Variational Approximation for Non-Conjugate Likelihoods, +# # a Classification Example +# +# +# This example demonstrates how to use augmentation to improve convergence speed +# for variational Gaussian process with non-conjugate likelihoods. +# inference using the stochastic variational Gaussian process (SVGP) model. For +# a basic introduction to the functionality of this library, please refer to the +# [User Guide](@ref). +# +# ## Setup + +using ApproximateGPs +using ParameterHandling +using Zygote +using Distributions +using LinearAlgebra +using Optim + +using Plots +default(; legend=:outertopright, size=(700, 400)) + +# We import AGP.jl for the Polya-Gamma distribution +using AugmentedGaussianProcesses.ComplementaryDistributions: PolyaGamma + +using Random +Random.seed!(1234); + +# ## Generate some training data +# +# We work in the same setting as the other [classification example](../b-classification). +# For our binary classification model, we will use the standard approach of a +# latent GP with a Bernoulli likelihood. This results in a generative model that +# we can use to produce some training data. +# +# First, we define the underlying latent GP +# ```math +# f \sim \mathcal{GP}(0, k(\cdot, \cdot')) +# ``` +# and sample a function `f`. + +k_true = [30.0, 1.5] +kernel_true = k_true[1] * (Matern32Kernel() ∘ ScaleTransform(k_true[2])) + +jitter = 1e-6 # for numeric stability +lik = BernoulliLikelihood() +lgp = LatentGP(GP(kernel_true), lik, jitter) +x_true = 0:0.02:6 +f_true, y_true = rand(lgp(x_true)) + +plot(x_true, f_true; seriescolor="red", label="") # Plot the sampled function + +# Then, the output of this sampled function is pushed through a logistic sigmoid +# `μ = σ(f)` to constrain the output to `[0, 1]`. + +μ = mean.(lgp.lik.(f_true)) +plot(x_true, μ; seriescolor="red", label="") + +# Finally, the outputs `y` of the process are sampled from a Bernoulli +# distribution with mean `μ`. We're only interested in the outputs at a subset +# of inputs `x`, so we first pick some random input locations and then find the +# corresponding values for `y`. + +N = 200 # The number of training points +mask = sample(1:length(x_true), N; replace=false, ordered=true) # Subsample some input locations +x, y = x_true[mask], y_true[mask] + +scatter(x, y; label="Sampled outputs") +plot!(x_true, mean.(lgp.lik.(f_true)); seriescolor="red", label="True mean") + +# Let's first see how we can obtain the optimal variational parameters +# for our variational distribution q(u) = N(m, S). +# We will for now use the true kernel parameters, +# We define the inducing points locations and the associated `FiniteGP` +M = 15; +z = range(0.1, 5.9; length=M); +fz = GP(kernel_true)(z, jitter); + +# We initialize the variational parameters q(u) = N(m, S) and q(ωᵢ) = p(ωᵢ) = PolyaGamma(1,0) +m = zeros(M); +S = Matrix{Float64}(I(M)); +qω = [PolyaGamma(1, 0.0) for _ in 1:length(x)]; + +# Following the derivations from the paper [Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation](https://arxiv.org/abs/1802.06383), +# we build the following `cavi!` function which will update the parameters `m` and `S` as well as the variables `ω` + +function cavi!(fz::AbstractGPs.FiniteGP, x, y, m, S, qω; niter=10) + K = ApproximateGPs._chol_cov(fz) + κ = K \ cov(fz.f, fz.x, x) + y_sign = sign.(y .- 0.5) + for _ in 1:niter + postu = posterior(SparseVariationalApproximation(Centered(), fz, MvNormal(m, S))) + postfs = marginals(postu(x)) + marginals_to_aug_posterior!(qω, postfs) + S .= inv(Symmetric(inv(K) + project(Diagonal(mean.(qω)), κ))) + m .= S * (project(y_sign / 2, κ) - K \ mean(fz)) + end + return m, S, qω +end + +function marginals_to_aug_posterior!(qω::AbstractVector, ps::AbstractVector{<:Normal}) + map!(qω, ps) do p + PolyaGamma(1, sqrt(abs2(mean(p)) + var(p))) + end +end + +# where `project` which project the noise to the right dimension +project(x::AbstractVector, κ) = κ * x +project(X::AbstractMatrix, κ) = κ * X * κ' + +# Let's now compare both results +post_svgp_init = posterior(SparseVariationalApproximation(Centered(), fz, MvNormal(m, S))) +cavi!(fz, x, y, m, S, qω) # m, S and qω are going to be modified +post_svgp_opt = posterior(SparseVariationalApproximation(Centered(), fz, MvNormal(m, S))) + +pf = scatter(x, y; legend=false, xlabel="x", ylabel="f") +plot!(x_true, f_true; seriescolor="red", label="True latent") +plot!(x_true, post_svgp_init(x_true); label="Initial Posterior") +plot!(x_true, post_svgp_opt(x_true); label="Final Posterior") +py = scatter(x, y; label="Training data", xlabel="x", ylabel="y") +plot!(x_true, mean.(lik.(f_true)); label="True mean") +plot!(x_true, mean.(lik.(mean(post_svgp_init(x_true)))); label="Initial Posterior") +plot!(x_true, mean.(lik.(mean(post_svgp_opt(x_true)))); label="Final Posterior") +plot(pf, py) +# ## Optimizing the hyperparameters + +# What if like in the [classical optimization case](../b-classification) +# we want to optimize hyperparameters as well? +# Let's use the same approach! +raw_initial_params = ( + k=(var=positive(rand()), precision=positive(rand())), + z=bounded.(range(0.1, 5.9; length=M), 0.0, 6.0), # constrain z to simplify optimisation +); +flat_init_params, unflatten = ParameterHandling.flatten(raw_initial_params) +unpack = ParameterHandling.value ∘ unflatten; + +# We now need to pass our variational parameters and modify the elbo +# to be able to infer correctly the results. +# This means we will have to implicitly pass the variational parameters +# `var_params` and update them after each update of the hyperparameters + +# ### The augmented ELBO +function augmented_elbo( + sva::SparseVariationalApproximation, + lfx::AbstractGPs.LatentFiniteGP, + y::AbstractVector, + qω::AbstractVector; + num_data=length(y), +) + post = posterior(sva) + q_f = marginals(post(lfx.fx.x)) + variational_exp = expected_aug_loglik(y, q_f, lfx.lik, qω) + + n_batch = length(y) + scale = num_data / n_batch + # We ignore the KL divergence regarding the augmented variables as it does + # not depend on the kernel parameters + return sum(variational_exp) * scale - Zygote.@ignore(kl_term(lik, qω)) - ApproximateGPs.kl_term(sva, post) +end + +function kl_term(::BernoulliLikelihood{<:LogisticLink}, qω::AbstractVector{<:PolyaGamma}) + sum(qω) do q + c = q.c + - abs2(c) * mean(q) + 2log(cosh(c / 2)) + end +end + +function expected_aug_loglik(y, q_f, ::BernoulliLikelihood, qω) + return map(y, q_f, qω) do y, q, qω + m = mean(q) + sign(y - 0.5) * m / 2 - (abs2(m) + var(q)) * mean(qω) / 2 - log(2) + end +end + +# ### The complete inference process +function loss(params::NamedTuple) + fz, lf = build_fz_lf(params) + fx = lf(x) + # We ignore this part as it involves inplace operations + # and does not contribute to the final elbo + Zygote.@ignore cavi!(fz, x, y, m, S, qω; niter=1) + augsvgp = SparseVariationalApproximation(Centered(), fz, MvNormal(m, Symmetric(S))) + return -augmented_elbo(augsvgp, fx, y, qω) +end + +function build_fz_lf(params::NamedTuple) + kernel = params.k.var * (Matern32Kernel() ∘ ScaleTransform(params.k.precision)) + lf = LatentGP(GP(kernel), lik, jitter) + fz = GP(kernel)(params.z) + return fz, lf +end + +opt = optimize( + loss ∘ unpack, + θ -> only(Zygote.gradient(loss ∘ unpack, θ)), + flat_init_params, + LBFGS(; + alphaguess=Optim.LineSearches.InitialStatic(; scaled=true), + linesearch=Optim.LineSearches.BackTracking(), + ), + Optim.Options(; iterations=100); + inplace=false, +) + +# And we can now compare with the optimal result we obtained earlier +θ_opt = unpack(opt.minimizer) +fz_opt, lf_opt = build_fz_lf(θ_opt) +post_svgp_hpopt = posterior(SparseVariationalApproximation(Centered(), fz_opt, MvNormal(m, S))) + +pf = scatter(x, y; legend=false, xlabel="x", ylabel="f") +plot!(x_true, f_true; seriescolor="red", label="True latent") +plot!(x_true, post_svgp_opt(x_true); label="Posterior") +plot!(x_true, post_svgp_hpopt(x_true); label="Posterior (hp opt)") +py = scatter(x, y; label="Training data", xlabel="x", ylabel="y") +plot!(x_true, mean.(lik.(f_true)); label="True mean") +plot!(x_true, mean.(lik.(mean(post_svgp_opt(x_true)))); label="Posterior") +plot!(x_true, mean.(lik.(mean(post_svgp_hpopt(x_true)))); label="Posterior (hp opt)") +plot(pf, py) \ No newline at end of file From dcef734c6df06ba01cf8e126aae1929e0fe26bb2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Galy-Fajou?= Date: Wed, 1 Dec 2021 18:17:53 +0100 Subject: [PATCH 2/8] Apply suggestions from code review Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- examples/d-augmented-gp/script.jl | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/examples/d-augmented-gp/script.jl b/examples/d-augmented-gp/script.jl index bbe5afba..0c3ed7ba 100644 --- a/examples/d-augmented-gp/script.jl +++ b/examples/d-augmented-gp/script.jl @@ -155,13 +155,14 @@ function augmented_elbo( scale = num_data / n_batch # We ignore the KL divergence regarding the augmented variables as it does # not depend on the kernel parameters - return sum(variational_exp) * scale - Zygote.@ignore(kl_term(lik, qω)) - ApproximateGPs.kl_term(sva, post) + return sum(variational_exp) * scale - Zygote.@ignore(kl_term(lik, qω)) - + ApproximateGPs.kl_term(sva, post) end function kl_term(::BernoulliLikelihood{<:LogisticLink}, qω::AbstractVector{<:PolyaGamma}) sum(qω) do q c = q.c - - abs2(c) * mean(q) + 2log(cosh(c / 2)) + -abs2(c) * mean(q) + 2log(cosh(c / 2)) end end @@ -205,7 +206,9 @@ opt = optimize( # And we can now compare with the optimal result we obtained earlier θ_opt = unpack(opt.minimizer) fz_opt, lf_opt = build_fz_lf(θ_opt) -post_svgp_hpopt = posterior(SparseVariationalApproximation(Centered(), fz_opt, MvNormal(m, S))) +post_svgp_hpopt = posterior( + SparseVariationalApproximation(Centered(), fz_opt, MvNormal(m, S)) +) pf = scatter(x, y; legend=false, xlabel="x", ylabel="f") plot!(x_true, f_true; seriescolor="red", label="True latent") From 4cffc9f751358893710f068ebf2e939867578378 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Galy-Fajou?= Date: Wed, 1 Dec 2021 18:18:12 +0100 Subject: [PATCH 3/8] Update script.jl --- examples/d-augmented-gp/script.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/d-augmented-gp/script.jl b/examples/d-augmented-gp/script.jl index 0c3ed7ba..94bee3f5 100644 --- a/examples/d-augmented-gp/script.jl +++ b/examples/d-augmented-gp/script.jl @@ -218,4 +218,4 @@ py = scatter(x, y; label="Training data", xlabel="x", ylabel="y") plot!(x_true, mean.(lik.(f_true)); label="True mean") plot!(x_true, mean.(lik.(mean(post_svgp_opt(x_true)))); label="Posterior") plot!(x_true, mean.(lik.(mean(post_svgp_hpopt(x_true)))); label="Posterior (hp opt)") -plot(pf, py) \ No newline at end of file +plot(pf, py) From 30b933ec54dd52d2d995f45fbd4b52e41b893c6d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Galy-Fajou?= Date: Wed, 1 Dec 2021 18:35:52 +0100 Subject: [PATCH 4/8] Use latest julia version for docs --- .github/workflows/Docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/Docs.yml b/.github/workflows/Docs.yml index 009fcdef..a032455f 100644 --- a/.github/workflows/Docs.yml +++ b/.github/workflows/Docs.yml @@ -14,7 +14,7 @@ jobs: - uses: actions/checkout@v2 - uses: julia-actions/setup-julia@latest with: - version: '1.6' + version: '1.7' - name: Install dependencies run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()' - name: Build and deploy From f23aa66152a580431858947168c1b2498fc1b1c5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Galy-Fajou?= Date: Wed, 1 Dec 2021 18:49:09 +0100 Subject: [PATCH 5/8] Moved comments out --- examples/d-augmented-gp/script.jl | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/examples/d-augmented-gp/script.jl b/examples/d-augmented-gp/script.jl index 94bee3f5..8aae3c37 100644 --- a/examples/d-augmented-gp/script.jl +++ b/examples/d-augmented-gp/script.jl @@ -153,11 +153,12 @@ function augmented_elbo( n_batch = length(y) scale = num_data / n_batch - # We ignore the KL divergence regarding the augmented variables as it does - # not depend on the kernel parameters + return sum(variational_exp) * scale - Zygote.@ignore(kl_term(lik, qω)) - ApproximateGPs.kl_term(sva, post) end +# We ignored the gradient of the KL divergence with respect to the augmented +# variables as it does not depend on the kernel parameters function kl_term(::BernoulliLikelihood{<:LogisticLink}, qω::AbstractVector{<:PolyaGamma}) sum(qω) do q @@ -177,13 +178,14 @@ end function loss(params::NamedTuple) fz, lf = build_fz_lf(params) fx = lf(x) - # We ignore this part as it involves inplace operations - # and does not contribute to the final elbo Zygote.@ignore cavi!(fz, x, y, m, S, qω; niter=1) augsvgp = SparseVariationalApproximation(Centered(), fz, MvNormal(m, Symmetric(S))) return -augmented_elbo(augsvgp, fx, y, qω) end +# We ignore the `cavi!` part as it involves inplace operations +# and does not contribute to the final elbo + function build_fz_lf(params::NamedTuple) kernel = params.k.var * (Matern32Kernel() ∘ ScaleTransform(params.k.precision)) lf = LatentGP(GP(kernel), lik, jitter) From d10de1366d60eb1f5a016e55dc675afbba72aa3b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Galy-Fajou?= Date: Wed, 1 Dec 2021 19:18:34 +0100 Subject: [PATCH 6/8] Ugly hack to avoid a call to blas? --- examples/b-classification/Manifest.toml | 537 ++++++++++++------------ src/expected_loglik.jl | 2 +- 2 files changed, 281 insertions(+), 258 deletions(-) diff --git a/examples/b-classification/Manifest.toml b/examples/b-classification/Manifest.toml index a8b06699..7cb8e9b6 100644 --- a/examples/b-classification/Manifest.toml +++ b/examples/b-classification/Manifest.toml @@ 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"dfd7a8f38d4613b6a575253b3174dd991ca6183e" uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e" version = "0.3.9+1" -[[Xorg_xcb_util_wm_jll]] +[[deps.Xorg_xcb_util_wm_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67" uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361" version = "0.4.1+1" -[[Xorg_xkbcomp_jll]] +[[deps.Xorg_xkbcomp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"] git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b" uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" version = "1.4.2+4" -[[Xorg_xkeyboard_config_jll]] +[[deps.Xorg_xkeyboard_config_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"] git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d" uuid = "33bec58e-1273-512f-9401-5d533626f822" version = "2.27.0+4" -[[Xorg_xtrans_jll]] +[[deps.Xorg_xtrans_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" -[[Zlib_jll]] +[[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -[[Zstd_jll]] +[[deps.Zstd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.5.0+0" -[[Zygote]] +[[deps.Zygote]] deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"] -git-tree-sha1 = "0fc9959bcabc4668c403810b4e851f6b8962eac9" +git-tree-sha1 = "76475a5aa0be302c689fd319cd257cd1a512fb3c" uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" -version = "0.6.29" +version = "0.6.32" -[[ZygoteRules]] +[[deps.ZygoteRules]] deps = ["MacroTools"] git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" uuid = "700de1a5-db45-46bc-99cf-38207098b444" version = "0.2.2" -[[libass_jll]] +[[deps.libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" -[[libfdk_aac_jll]] +[[deps.libblastrampoline_jll]] +deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" + +[[deps.libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" -[[libpng_jll]] +[[deps.libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.38+0" -[[libvorbis_jll]] +[[deps.libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+0" -[[nghttp2_jll]] +[[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -[[p7zip_jll]] +[[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -[[x264_jll]] +[[deps.x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" -[[x265_jll]] +[[deps.x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" -[[xkbcommon_jll]] +[[deps.xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" diff --git a/src/expected_loglik.jl b/src/expected_loglik.jl index c29ff3bb..f78eaa91 100644 --- a/src/expected_loglik.jl +++ b/src/expected_loglik.jl @@ -75,7 +75,7 @@ function expected_loglik( # size(fs): (length(y), n_points) fs = √2 * std.(q_f) .* xs' .+ mean.(q_f) lls = loglikelihood.(lik.(fs), y) - return sum((1 / √π) * lls * ws) + return (1 / √π) * mapreduce(dot, +, eachrow(lls), ws) end ChainRulesCore.@non_differentiable gausshermite(n) From 05349eb50ac06af20e90ca4a73c7b3d2f65ac24e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Galy-Fajou?= Date: Wed, 8 Dec 2021 14:20:30 +0100 Subject: [PATCH 7/8] Switch back to original definition --- src/expected_loglik.jl | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/expected_loglik.jl b/src/expected_loglik.jl index f78eaa91..dd96108d 100644 --- a/src/expected_loglik.jl +++ b/src/expected_loglik.jl @@ -72,10 +72,9 @@ function expected_loglik( # using a reparameterisation by change of variable # (see e.g. en.wikipedia.org/wiki/Gauss%E2%80%93Hermite_quadrature) xs, ws = gausshermite(gh.n_points) - # size(fs): (length(y), n_points) - fs = √2 * std.(q_f) .* xs' .+ mean.(q_f) + fs = sqrt(2) * std.(q_f) .* xs' .+ mean.(q_f) lls = loglikelihood.(lik.(fs), y) - return (1 / √π) * mapreduce(dot, +, eachrow(lls), ws) + return sum(lls * ws) / sqrt(π) end ChainRulesCore.@non_differentiable gausshermite(n) From b19e62d83807c6d68d3e62a46e47742abc14c04e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Galy-Fajou?= Date: Wed, 8 Dec 2021 14:59:19 +0100 Subject: [PATCH 8/8] Updated Manifest --- examples/b-classification/Manifest.toml | 26 ++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/examples/b-classification/Manifest.toml b/examples/b-classification/Manifest.toml index 7cb8e9b6..93de88a1 100644 --- a/examples/b-classification/Manifest.toml +++ b/examples/b-classification/Manifest.toml @@ -56,15 +56,15 @@ version = "1.16.1+0" [[deps.ChainRules]] deps = ["ChainRulesCore", "Compat", "LinearAlgebra", "Random", "RealDot", "Statistics"] -git-tree-sha1 = "65a0826ac25869556070076459925d1700316aab" +git-tree-sha1 = "d9d08f88759465c7895db73d052c23e5c260f4a2" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" -version = "1.14.0" +version = "1.15.0" [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "f885e7e7c124f8c92650d61b9477b9ac2ee607dd" +git-tree-sha1 = "4c26b4e9e91ca528ea212927326ece5918a04b47" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" -version = "1.11.1" +version = "1.11.2" [[deps.ChangesOfVariables]] deps = ["LinearAlgebra", "Test"] @@ -160,10 +160,10 @@ uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" version = "1.5.0" [[deps.Distances]] -deps = ["LinearAlgebra", "Statistics", "StatsAPI"] -git-tree-sha1 = "837c83e5574582e07662bbbba733964ff7c26b9d" +deps = ["LinearAlgebra", "SparseArrays", "Statistics", "StatsAPI"] +git-tree-sha1 = "3258d0659f812acde79e8a74b11f17ac06d0ca04" uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" -version = "0.10.6" +version = "0.10.7" [[deps.Distributed]] deps = ["Random", "Serialization", "Sockets"] @@ -306,9 +306,9 @@ version = "0.21.0+0" [[deps.Glib_jll]] deps = ["Artifacts", "Gettext_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Libiconv_jll", "Libmount_jll", "PCRE_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "7bf67e9a481712b3dbe9cb3dac852dc4b1162e02" +git-tree-sha1 = "74ef6288d071f58033d54fd6708d4bc23a8b8972" uuid = "7746bdde-850d-59dc-9ae8-88ece973131d" -version = "2.68.3+0" +version = "2.68.3+1" [[deps.Graphite2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -372,9 +372,9 @@ uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" version = "0.1.1" [[deps.IterTools]] -git-tree-sha1 = "05110a2ab1fc5f932622ffea2a003221f4782c18" +git-tree-sha1 = "fa6287a4469f5e048d763df38279ee729fbd44e5" uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e" -version = "1.3.0" +version = "1.4.0" [[deps.IteratorInterfaceExtensions]] git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" @@ -674,9 +674,9 @@ version = "1.0.15" [[deps.Plots]] deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "GeometryBasics", "JSON", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "PlotThemes", "PlotUtils", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun"] -git-tree-sha1 = "9c52b09e45f9926a8e50fb7d63f25066431c2e02" +git-tree-sha1 = "3e7e9415f917db410dcc0a6b2b55711df434522c" uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -version = "1.24.4" +version = "1.25.1" [[deps.PositiveFactorizations]] deps = ["LinearAlgebra"]