From fc8cce639766ad7c93a620ee61dde51b06ca937d Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Thu, 19 Feb 2026 12:21:55 +0000 Subject: [PATCH 1/7] implement get_hybridproblem_correlation_Ms --- Project.toml | 2 - docs/.gitignore | 2 + docs/src/tutorials/Manifest-v1.11.toml | 3398 +++++++++++++++++ docs/src/tutorials/Manifest-v1.12.toml | 24 +- docs/src/tutorials/basic_cpu.md | 7 + docs/src/tutorials/basic_cpu.qmd | 7 + docs/src/tutorials/inspect_results.md | 21 + docs/src/tutorials/inspect_results.qmd | 27 +- .../intermediate/basic_cpu_results.jld2 | Bin 180818 -> 180818 bytes src/AbstractHybridProblem.jl | 3 +- src/HybridProblem.jl | 37 +- src/HybridVariationalInference.jl | 1 + src/elbo.jl | 17 + src/gf.jl | 2 +- 14 files changed, 3529 insertions(+), 19 deletions(-) create mode 100644 docs/src/tutorials/Manifest-v1.11.toml diff --git a/Project.toml b/Project.toml index fec6be5..2fd4b1b 100644 --- a/Project.toml +++ b/Project.toml @@ -38,7 +38,6 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" Lux = "b2108857-7c20-44ae-9111-449ecde12c47" -SimpleChains = "de6bee2f-e2f4-4ec7-b6ed-219cc6f6e9e5" [extensions] HybridVariationalInferenceCUDAExt = "CUDA" @@ -72,7 +71,6 @@ NaNMath = "1.1.3" Optimisers = "0.4.6" Optimization = "3.11, 4" Random = "1.10.0" -SimpleChains = "0.4" StableRNGs = "1.0.2" StaticArrays = "1.9.13" StatsBase = "0.34.4" diff --git a/docs/.gitignore b/docs/.gitignore index 075b254..0e3521a 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -1 +1,3 @@ /.quarto/ + +**/*.quarto_ipynb diff --git a/docs/src/tutorials/Manifest-v1.11.toml b/docs/src/tutorials/Manifest-v1.11.toml new file mode 100644 index 0000000..4f4e554 --- /dev/null +++ b/docs/src/tutorials/Manifest-v1.11.toml @@ -0,0 +1,3398 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.11.9" +manifest_format = "2.0" +project_hash = 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"HarfBuzz_jll", "JLLWrappers", "Libdl", "Zlib_jll"] +git-tree-sha1 = "125eedcb0a4a0bba65b657251ce1d27c8714e9d6" +uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" +version = "0.17.4+0" + +[[deps.libblastrampoline_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" +version = "5.11.0+0" + +[[deps.libdecor_jll]] +deps = ["Artifacts", "Dbus_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pango_jll", "Wayland_jll", "xkbcommon_jll"] +git-tree-sha1 = "9bf7903af251d2050b467f76bdbe57ce541f7f4f" +uuid = "1183f4f0-6f2a-5f1a-908b-139f9cdfea6f" +version = "0.2.2+0" + +[[deps.libevdev_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "56d643b57b188d30cccc25e331d416d3d358e557" +uuid = "2db6ffa8-e38f-5e21-84af-90c45d0032cc" +version = "1.13.4+0" + +[[deps.libfdk_aac_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "646634dd19587a56ee2f1199563ec056c5f228df" +uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" +version = "2.0.4+0" + 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"Libglvnd_jll", "Libtiff_jll", "libpng_jll"] +git-tree-sha1 = "4e4282c4d846e11dce56d74fa8040130b7a95cb3" +uuid = "c5f90fcd-3b7e-5836-afba-fc50a0988cb2" +version = "1.6.0+0" + +[[deps.mtdev_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "b4d631fd51f2e9cdd93724ae25b2efc198b059b1" +uuid = "009596ad-96f7-51b1-9f1b-5ce2d5e8a71e" +version = "1.1.7+0" + +[[deps.nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.59.0+0" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+2" + +[[deps.x264_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "14cc7083fc6dff3cc44f2bc435ee96d06ed79aa7" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "10164.0.1+0" + +[[deps.x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "e7b67590c14d487e734dcb925924c5dc43ec85f3" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "4.1.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "a1fc6507a40bf504527d0d4067d718f8e179b2b8" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.13.0+0" diff --git a/docs/src/tutorials/Manifest-v1.12.toml b/docs/src/tutorials/Manifest-v1.12.toml index e3d8c46..0a80e6a 100644 --- a/docs/src/tutorials/Manifest-v1.12.toml +++ b/docs/src/tutorials/Manifest-v1.12.toml @@ -1,8 +1,8 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.12.0" +julia_version = "1.12.5" manifest_format = "2.0" -project_hash = "b7ca4e9d809facc5760954c617658f1ee144f372" +project_hash = "5313db7951d8d244962549993121abb3638d8442" [[deps.ADTypes]] git-tree-sha1 = "27cecae79e5cc9935255f90c53bb831cc3c870d7" @@ -739,7 +739,7 @@ version = "0.9.5" [[deps.Downloads]] deps = ["ArgTools", "FileWatching", "LibCURL", "NetworkOptions"] uuid = "f43a241f-c20a-4ad4-852c-f6b1247861c6" -version = "1.6.0" +version = "1.7.0" [[deps.EarCut_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1087,7 +1087,7 @@ uuid = "3e5b6fbb-0976-4d2c-9146-d79de83f2fb0" version = "0.1.17" [[deps.HybridVariationalInference]] -deps = ["Bijectors", "BlockDiagonals", "ChainRulesCore", "Combinatorics", "CommonSolve", "ComponentArrays", "DifferentiationInterface", "DistributionFits", "Distributions", "FillArrays", "Functors", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "LogExpFunctions", "MLDataDevices", "MLUtils", "Missings", "NaNMath", "Optimisers", "Optimization", "Random", "StableRNGs", "StaticArrays", "StatsBase", "StatsFuns", "Test", "Zygote"] +deps = ["Bijectors", "BlockDiagonals", "ChainRulesCore", "Combinatorics", "CommonSolve", "ComponentArrays", "DifferentiationInterface", "DistributionFits", "Distributions", "FillArrays", "Functors", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "LogExpFunctions", "MLDataDevices", "MLUtils", "Missings", "NaNMath", "Optimisers", "Optimization", "Random", "StableRNGs", "StaticArrays", "StatsBase", "StatsFuns", "Test", "UnPack", "Zygote"] path = "../../.." uuid = "a108c475-a4e2-4021-9a84-cfa7df242f64" version = "0.2.0" @@ -1469,7 +1469,7 @@ version = "0.6.4" [[deps.LibCURL_jll]] deps = ["Artifacts", "LibSSH2_jll", "Libdl", "OpenSSL_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "8.11.1+1" +version = "8.15.0+0" [[deps.LibGit2]] deps = ["LibGit2_jll", "NetworkOptions", "Printf", "SHA"] @@ -1835,7 +1835,7 @@ version = "0.3.7" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" -version = "2025.5.20" +version = "2025.11.4" [[deps.NNlib]] deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Random", "ScopedValues", "Statistics"] @@ -1952,7 +1952,7 @@ version = "1.5.0" [[deps.OpenSSL_jll]] deps = ["Artifacts", "Libdl"] uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "3.5.1+0" +version = "3.5.4+0" [[deps.OpenSpecFun_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl"] @@ -2093,7 +2093,7 @@ version = "0.44.2+0" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "Random", "SHA", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" -version = "1.12.0" +version = "1.12.1" weakdeps = ["REPL"] [deps.Pkg.extensions] @@ -3226,7 +3226,7 @@ version = "0.17.4+0" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.13.1+1" +version = "5.15.0+0" [[deps.libdecor_jll]] deps = ["Artifacts", "Dbus_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pango_jll", "Wayland_jll", "xkbcommon_jll"] @@ -3288,15 +3288,15 @@ uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" version = "1.64.0+1" [[deps.oneTBB_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] +deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl"] git-tree-sha1 = "d5a767a3bb77135a99e433afe0eb14cd7f6914c3" uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e" version = "2022.0.0+0" [[deps.p7zip_jll]] -deps = ["Artifacts", "Libdl"] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -version = "17.5.0+2" +version = "17.7.0+0" [[deps.x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] diff --git a/docs/src/tutorials/basic_cpu.md b/docs/src/tutorials/basic_cpu.md index b8031ac..d9efda7 100644 --- a/docs/src/tutorials/basic_cpu.md +++ b/docs/src/tutorials/basic_cpu.md @@ -5,6 +5,12 @@ CurrentModule = HybridVariationalInference ``` +Julias version: + +``` julia +VERSION +``` + First load necessary packages. ``` julia @@ -288,6 +294,7 @@ For the parameters, one row corresponds to one site. For the drivers and predictions, one column corresponds to one site. ``` julia +using StaticArrays function f_doubleMM_sites(θc::CA.ComponentMatrix, xPc::CA.ComponentMatrix) # extract several covariates from xP S1 = view(xPc, Val(:S1), :) diff --git a/docs/src/tutorials/basic_cpu.qmd b/docs/src/tutorials/basic_cpu.qmd index 6e3389d..0b46ebe 100644 --- a/docs/src/tutorials/basic_cpu.qmd +++ b/docs/src/tutorials/basic_cpu.qmd @@ -1,6 +1,8 @@ --- title: "Basic workflow without GPU" engine: julia +julia: + exeflags: ["+1.11"] execute: echo: true output: false @@ -16,6 +18,11 @@ bibliography: twutz_txt.bib CurrentModule = HybridVariationalInference ``` +Julias version: +```{julia} +VERSION +``` + First load necessary packages. ```{julia} using HybridVariationalInference diff --git a/docs/src/tutorials/inspect_results.md b/docs/src/tutorials/inspect_results.md index c0f5ef9..a12a42d 100644 --- a/docs/src/tutorials/inspect_results.md +++ b/docs/src/tutorials/inspect_results.md @@ -104,6 +104,23 @@ We see that $K_1$ across sites ranges from about 0.18 to 0.25, and that its estimated uncertainty is about 0.04, slightly decreasing with the values of the parameter. +## Correlations among site parameters at uncronstrained scale + +The features a correlation matrix of site parameters at unconstrained scale. +It can be extracted using function [`get_hybridproblem_correlation_Ms`](@ref). + +In a first implementation, this function operates on an `AbstractHybridProblem` +assuming that its returned `ϕq` contains a component `ρsM` +that is used to parameterize the +Cholesky factor of the correlation matrix. + +``` julia +CM = HVI.get_hybridproblem_correlation_Ms(probo) +fig = Figure(); ax = Axis(fig[1,1], xlabel="mean($par)",ylabel="sd($par)") +scatter!(ax, θmean, θsd) +fig +``` + ## Predictive Posterior In addition to the uncertainty in parameters, we are also interested in @@ -145,3 +162,7 @@ fig We see that observed values for associated substrate concentrations range about from 0.51 to 0.59 with an estimated standard deviation around 0.005 that decreases with the observed value. + +If only a point prediction is required, function [`predict_point_hvi`](@ref) +can be used, that returns +a single set of expected parameters and corresponding predictions. diff --git a/docs/src/tutorials/inspect_results.qmd b/docs/src/tutorials/inspect_results.qmd index 92bd94b..c85aecd 100644 --- a/docs/src/tutorials/inspect_results.qmd +++ b/docs/src/tutorials/inspect_results.qmd @@ -47,10 +47,15 @@ probo = load(fname, "probo"); # not necessary any more with DoubleMM.f_doubleMM_sites # {{< include _pbm_matrix.qmd >}} # outside notebook, need to reset ModelApplicator, due to fθ defined in Notebook module -#θFix = CA.ComponentVector{eltype(probo.θP)}(r0=0.3) +# θFix = CA.ComponentVector{eltype(probo.θP)}( r0=probo.f_allsites.θFixm[1]) -_xP_batch = first(probo.train_dataloader)[2] +_xP_batch = _xP = first(probo.train_dataloader)[2] +_xM = first(probo.train_dataloader)[1] +_xM = probo.train_dataloader.data[1] +_xP = probo.train_dataloader.data[2] +tmp = predict_point_hvi(rng, probo, xM = _xM, xP = _xP) + f_batch = PBMPopulationApplicator( f_doubleMM_sites, probo.n_batch; probo.θP, probo.θM, θFix, xPvec=_xP_batch[:,1]) probo = HybridProblem(probo; f_batch) @@ -128,6 +133,21 @@ We see that $K_1$ across sites ranges from about 0.18 to 0.25, and that its estimated uncertainty is about 0.04, slightly decreasing with the values of the parameter. +## Correlations among site parameters at uncronstrained scale +The features a correlation matrix of site parameters at unconstrained scale. +It can be extracted using function [`get_hybridproblem_correlation_Ms`](@ref). + +In a first implementation, this function operates on an `AbstractHybridProblem` +assuming that its returned `ϕq` contains a component `ρsM` +that is used to parameterize the +Cholesky factor of the correlation matrix. + +``` julia +CM = HVI.get_hybridproblem_correlation_Ms(probo) +fig = Figure(); ax = Axis(fig[1,1], xlabel="mean($par)",ylabel="sd($par)") +scatter!(ax, θmean, θsd) +fig +``` ## Predictive Posterior In addition to the uncertainty in parameters, we are also interested in @@ -168,6 +188,9 @@ We see that observed values for associated substrate concentrations range about with the observed value. +If only a point prediction is required, function [`predict_point_hvi`](@ref) +can be used, that returns +a single set of expected parameters and corresponding predictions. diff --git a/docs/src/tutorials/intermediate/basic_cpu_results.jld2 b/docs/src/tutorials/intermediate/basic_cpu_results.jld2 index 17f8cc2cc59e140d1e456cec44ca2bd451cc5639..327915193dbcb20e5e8e08d50bb3836cc0dc7da4 100644 GIT binary patch delta 2398 zcmV~$2_V#I9{^y)B%-ZIqxw*WNQV|H^M8L6N<~H_$!;mOqEK{6%QVVWqcnxeEoT>6 zRObKwCZ)ct89CxBUF6tZidI=s>3g1*liDpOwNqB8uBdQG3lN1;cNFf}jMrS+Apcbj z^)X}d>=rk4e-a4nunsow(uC$@E2hTzJ=57Vi2i@};U%dOXC>KyAuR{^9tOa3$diS#V)hlf7x=;LQ9fB9l5t60v5DZjIH{8=@G^1L9Z 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z6`e)D^2=;6SIeI)JrV*!m)$tH_$aaOSqp9u3l*QV#Htz2;4;JEpLWr<%o?wjmc5~| n@A9&R?p*cyC( Date: Sat, 21 Feb 2026 08:02:38 +0000 Subject: [PATCH 2/7] store test data with Problem --- Project.toml | 2 + docs/src/tutorials/basic_cpu.md | 20 ++++++--- docs/src/tutorials/basic_cpu.qmd | 18 +++++--- .../intermediate/basic_cpu_results.jld2 | Bin 180818 -> 196159 bytes src/AbstractHybridProblem.jl | 12 ++++-- src/DoubleMM/f_doubleMM.jl | 40 ++++++++++++------ src/HybridProblem.jl | 15 +++++-- src/HybridVariationalInference.jl | 1 + src/elbo.jl | 8 ++-- test/test_HybridProblem.jl | 14 ++++-- 10 files changed, 89 insertions(+), 41 deletions(-) diff --git a/Project.toml b/Project.toml index 2fd4b1b..7a5a80d 100644 --- a/Project.toml +++ b/Project.toml @@ -38,6 +38,7 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" Lux = "b2108857-7c20-44ae-9111-449ecde12c47" +SimpleChains = "de6bee2f-e2f4-4ec7-b6ed-219cc6f6e9e5" [extensions] HybridVariationalInferenceCUDAExt = "CUDA" @@ -71,6 +72,7 @@ NaNMath = "1.1.3" Optimisers = "0.4.6" Optimization = "3.11, 4" Random = "1.10.0" +SimpleChains = "0.4.8" StableRNGs = "1.0.2" StaticArrays = "1.9.13" StatsBase = "0.34.4" diff --git a/docs/src/tutorials/basic_cpu.md b/docs/src/tutorials/basic_cpu.md index d9efda7..a425e56 100644 --- a/docs/src/tutorials/basic_cpu.md +++ b/docs/src/tutorials/basic_cpu.md @@ -131,17 +131,25 @@ Here, we use synthetic data generated by the package. ``` julia rng = StableRNG(111) +n_site_test = 60 (; xM, xP, y_o, y_unc) = gen_hybridproblem_synthetic( - rng, DoubleMM.DoubleMMCase(); scenario=Val((:omit_r0,))) + rng, DoubleMM.DoubleMMCase(); n_site_test, scenario=Val((:omit_r0,))) +n_site = size(y_o,2) - n_site_test +i_test = n_site .+ (1:n_site_test) +i_train = 1:n_site +test_data = (; xM = xM[:, i_test], xP = xP[:, i_test], + y_o = y_o[:, i_test], y_unc = y_unc[:, i_test], i_site = i_test) +train_data = (; xM = xM[:, i_train], xP = xP[:, i_train], + y_o = y_o[:, i_train], y_unc = y_unc[:, i_train], i_site = i_train) ``` Lets look at them. ``` julia -size(xM), size(xP), size(y_o), size(y_unc) +map(size,train_data) ``` - ((5, 800), (16, 800), (8, 800), (8, 800)) + (xM = (5, 800), xP = (16, 800), y_o = (8, 800), y_unc = (8, 800), i_site = (800,)) All of them have 800 columns, corresponding to 800 sites. There are 5 site-covaritas, 16 values of model drivers, and 8 observations per site. @@ -167,11 +175,9 @@ iteration of the solver. In addition to the data, it provides an index to the sites inside a tuple. ``` julia -n_site = size(y_o,2) n_batch = 20 train_dataloader = MLUtils.DataLoader( - (CA.getdata(xM), CA.getdata(xP), y_o, y_unc, 1:n_site), - batchsize=n_batch, partial=false) + values(train_data), batchsize=n_batch, partial=false) ``` ## The Machine-Learning model @@ -241,7 +247,7 @@ f_batch = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1]) prob = HybridProblem(θM, ϕq0, g_chain_scaled, ϕg0, f_batch, priors_dict, py, - transM, transP, train_dataloader, n_covar, n_site, n_batch) + transM, transP, train_dataloader, test_data,n_covar, n_site, n_batch) ``` ## Perform the inversion diff --git a/docs/src/tutorials/basic_cpu.qmd b/docs/src/tutorials/basic_cpu.qmd index 0b46ebe..b347a43 100644 --- a/docs/src/tutorials/basic_cpu.qmd +++ b/docs/src/tutorials/basic_cpu.qmd @@ -137,8 +137,16 @@ Here, we use synthetic data generated by the package. ```{julia} rng = StableRNG(111) +n_site_test = 60 (; xM, xP, y_o, y_unc) = gen_hybridproblem_synthetic( - rng, DoubleMM.DoubleMMCase(); scenario=Val((:omit_r0,))) + rng, DoubleMM.DoubleMMCase(); n_site_test, scenario=Val((:omit_r0,))) +n_site = size(y_o,2) - n_site_test +i_test = n_site .+ (1:n_site_test) +i_train = 1:n_site +test_data = (; xM = xM[:, i_test], xP = xP[:, i_test], + y_o = y_o[:, i_test], y_unc = y_unc[:, i_test], i_site = i_test) +train_data = (; xM = xM[:, i_train], xP = xP[:, i_train], + y_o = y_o[:, i_train], y_unc = y_unc[:, i_train], i_site = i_train) ``` ```{julia} @@ -153,7 +161,7 @@ end Lets look at them. ```{julia} #| output: true -size(xM), size(xP), size(y_o), size(y_unc) +map(size,train_data) ``` All of them have 800 columns, corresponding to 800 sites. There are 5 site-covaritas, 16 values of model drivers, and 8 observations per site. @@ -177,11 +185,9 @@ iteration of the solver. In addition to the data, it provides an index to the sites inside a tuple. ```{julia} -n_site = size(y_o,2) n_batch = 20 train_dataloader = MLUtils.DataLoader( - (CA.getdata(xM), CA.getdata(xP), y_o, y_unc, 1:n_site), - batchsize=n_batch, partial=false) + values(train_data), batchsize=n_batch, partial=false) ``` ## The Machine-Learning model @@ -252,7 +258,7 @@ f_batch = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1]) prob = HybridProblem(θM, ϕq0, g_chain_scaled, ϕg0, f_batch, priors_dict, py, - transM, transP, train_dataloader, n_covar, n_site, n_batch) + transM, transP, train_dataloader, test_data,n_covar, n_site, n_batch) ``` ```{julia} diff --git a/docs/src/tutorials/intermediate/basic_cpu_results.jld2 b/docs/src/tutorials/intermediate/basic_cpu_results.jld2 index 327915193dbcb20e5e8e08d50bb3836cc0dc7da4..31d167c1f2efc6ced0d239dfea44ad3ffb856e9a 100644 GIT binary patch literal 196159 zcmeFa2UHcw@;H16Dheu!C_bHFSrcg#5} 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`get_hybridproblem_train_dataloader` (may use `construct_dataloader_from_synthetic`) +- `get_hybridproblem_test_data` - `get_hybridproblem_priors` - `get_hybridproblem_n_covar` - `get_hybridproblem_n_site_and_batch` @@ -150,7 +151,7 @@ function get_hybridproblem_n_site_and_batch end """ - gen_hybridproblem_synthetic([rng,] ::AbstractHybridProblem; scenario) + gen_hybridproblem_synthetic([rng,] ::AbstractHybridProblem; n_site_test = 0, scenario) Setup synthetic data, a NamedTuple of - xM: matrix of covariates, with one column per site @@ -173,6 +174,7 @@ end """ get_hybridproblem_train_dataloader(::AbstractHybridProblem; scenario, n_batch) + get_hybridproblem_test_data end::AbstractHybridProblem; scenario) Return a DataLoader that provides a tuple of - `xM`: matrix of covariates, with one column per site @@ -182,6 +184,8 @@ Return a DataLoader that provides a tuple of - `i_sites`: Vector of indices of sites in the minibatch """ function get_hybridproblem_train_dataloader end +function get_hybridproblem_test_data end + """ construct_dataloader_from_synthetic(rng::AbstractRNG, prob::AbstractHybridProblem; @@ -190,11 +194,11 @@ function get_hybridproblem_train_dataloader end Construct a dataloader based on `gen_hybridproblem_synthetic`. """ function construct_dataloader_from_synthetic(rng::AbstractRNG, prob::AbstractHybridProblem; - scenario = (), n_batch, + scenario = (), n_batch, n_site_test = 0, #gdev = :use_gpu ∈ scenario ? gpu_device() : identity, ) - (; xM, xP, y_o, y_unc) = gen_hybridproblem_synthetic(rng, prob; scenario) - n_site = size(xM,2) + (; xM, xP, y_o, y_unc) = gen_hybridproblem_synthetic(rng, prob; n_site_test, scenario) + n_site = size(xM,2) - n_site_test @assert size(xP,2) == n_site @assert size(y_o,2) == n_site @assert size(y_unc,2) == n_site diff --git a/src/DoubleMM/f_doubleMM.jl b/src/DoubleMM/f_doubleMM.jl index f950ebb..20818cf 100644 --- a/src/DoubleMM/f_doubleMM.jl +++ b/src/DoubleMM/f_doubleMM.jl @@ -376,37 +376,51 @@ function HVI.get_hybridproblem_train_dataloader(prob::DoubleMMCase; scenario::Va rng::AbstractRNG = StableRNG(111), kwargs... ) where {scen} n_site, n_batch = get_hybridproblem_n_site_and_batch(prob; scenario) + dl = construct_dataloader_from_synthetic(rng, prob; scenario, n_batch, kwargs...) if (:driverNAN ∈ scen) - (; xM, xP, y_o, y_unc) = gen_hybridproblem_synthetic(rng, prob; scenario) - n_site = size(xM,2) - i_sites = 1:n_site + (xM, xP, y_o, y_unc, i_sites) = dl.data # set the last two entries of the S1 drivers and observations of the second site NaN - view(@view(xP[:S1,2]), 7:8) .= NaN - y_o[7:8,2] .= NaN + is_obs = 7:8 + i_site = 2 + xP[is_obs,i_site] .= NaN + y_o[is_obs,i_site] .= NaN train_loader = MLUtils.DataLoader((CA.getdata(xM), CA.getdata(xP), y_o, y_unc, i_sites); batchsize = n_batch, partial = false) else - construct_dataloader_from_synthetic(rng, prob; scenario, n_batch, kwargs...) + dl end end +function HVI.get_hybridproblem_test_data(prob::DoubleMMCase; scenario::Val{scen}, + rng::AbstractRNG = StableRNG(211), kwargs... +) where {scen} + n_site_test = 60 + (; xM, xP, y_o, y_unc) = gen_hybridproblem_synthetic( + rng, prob; scenario, n_site_test) + n_site_all = size(xM,2) + i_test = (n_site_all - n_site_test + 1):n_site_all + (; xM = xM[:, i_test], xP = xP[:, i_test], y_o = y_o[:, i_test], + y_unc = y_unc[:, i_test], i_sites = i_test) +end + function HVI.gen_hybridproblem_synthetic(rng::AbstractRNG, prob::DoubleMMCase; - scenario::Val{scen}) where {scen} + scenario::Val{scen}, n_site_test = 0) where {scen} n_covar_pc = 2 n_site, n_batch = get_hybridproblem_n_site_and_batch(prob; scenario) + n_siteall = n_site + n_site_test n_covar = get_hybridproblem_n_covar(prob; scenario) n_θM = length(θM) FloatType = get_hybridproblem_float_type(prob; scenario) - xM, θMs_true0 = gen_cov_pred(rng, FloatType, n_covar_pc, n_covar, n_site, n_θM; + xM, θMs_true0 = gen_cov_pred(rng, FloatType, n_covar_pc, n_covar, n_siteall, n_θM; rhodec = 8, is_using_dropout = false) - int_θMs_sites = ComponentArrayInterpreter(θM, (n_site,)) + int_θMs_sites = ComponentArrayInterpreter(θM, (n_siteall,)) # normalize to be distributed around the prescribed true values θMs_true = int_θMs_sites(scale_centered_at(θMs_true0, θM, FloatType(0.1))) f_batch = get_hybridproblem_PBmodel(prob; scenario) - f = create_nsite_applicator(f_batch, n_site) - #xP = fill((; S1 = xP_S1, S2 = xP_S2), n_site) - int_xP_sites = ComponentArrayInterpreter(int_xP1, (n_site,)) - xP = int_xP_sites(vcat(repeat(xP_S1, 1, n_site), repeat(xP_S2, 1, n_site))) + f = create_nsite_applicator(f_batch, n_siteall) + #xP = fill((; S1 = xP_S1, S2 = xP_S2), n_siteall) + int_xP_sites = ComponentArrayInterpreter(int_xP1, (n_siteall,)) + xP = int_xP_sites(vcat(repeat(xP_S1, 1, n_siteall), repeat(xP_S2, 1, n_siteall))) #xP[:S1,:] #θP = get_θP(prob) # for DoubleMMCase par_templates gives correct θP θP = get_hybridproblem_θP(prob; scenario) diff --git a/src/HybridProblem.jl b/src/HybridProblem.jl index 3eaf963..6c75001 100644 --- a/src/HybridProblem.jl +++ b/src/HybridProblem.jl @@ -11,9 +11,10 @@ Fields: - `py`: Likelihood function - `transM::Stacked`, `transP::Stacked`: bijectors transforming from unconstrained to constrained scale for site-specific and global parameters respectively. -- `train_dataloader::MLUtils.DataLoader`: providingn Tuple of matrices +- `train_dataloader::MLUtils.DataLoader`: providing Tuple of matrices `(xM, xP, y_o, y_unc, i_sites)`: covariates, model drivers, observations, observation uncertainties and index of provided sites. +- `test_data::Tuple of the same form as with `train_dataloader` for testset data. - `n_covar::Int`, `n_site::Int`, `n_batch::Int`: number covariates, number of sites, and number of sites within one batch - `cor_ends::NamedTuple`: block structure in correlations, @@ -35,6 +36,7 @@ struct HybridProblem <: AbstractHybridProblem transP::Stacked cor_ends::@NamedTuple{P::Vector{Int}, M::Vector{Int}} # = (P=(1,),M=(1,)) train_dataloader::MLUtils.DataLoader + test_data::NamedTuple n_covar::Int n_site::Int n_batch::Int @@ -51,8 +53,8 @@ struct HybridProblem <: AbstractHybridProblem py, transM::Stacked, transP::Stacked, - # return a function that constructs the trainloader based on n_batch train_dataloader::MLUtils.DataLoader, + test_data::NamedTuple, n_covar::Int, n_site::Int, n_batch::Int, @@ -62,7 +64,7 @@ struct HybridProblem <: AbstractHybridProblem ) where N new( θM, f_batch, g, ϕg, ϕq, priors, py, transM, transP, cor_ends, - train_dataloader, n_covar, n_site, n_batch, pbm_covars, approx) + train_dataloader, test_data, n_covar, n_site, n_batch, pbm_covars, approx) end end @@ -112,6 +114,7 @@ function update_hybridProblem(prob::AbstractHybridProblem; scenario, transP = get_hybridproblem_transforms(prob; scenario).transP, transM = get_hybridproblem_transforms(prob; scenario).transM, train_dataloader = get_hybridproblem_train_dataloader(prob; scenario), + test_data = get_hybridproblem_test_data(prob; scenario), n_covar = get_hybridproblem_n_covar(prob; scenario), n_site = get_hybridproblem_n_site_and_batch(prob; scenario)[1], n_batch = get_hybridproblem_n_site_and_batch(prob; scenario)[2], @@ -137,7 +140,7 @@ function update_hybridProblem(prob::AbstractHybridProblem; scenario, ϕq = CA.ComponentVector(ϕq; ϕunc...) end HybridProblem(θM, ϕq, g, ϕg, f_batch, priors, py, transM, transP, train_dataloader, - n_covar, n_site, n_batch, cor_ends_new, pbm_covars, approx) + test_data, n_covar, n_site, n_batch, cor_ends_new, pbm_covars, approx) end function HybridProblem(prob::HybridProblem; kwargs... ) @@ -245,6 +248,10 @@ function get_hybridproblem_train_dataloader(prob::HybridProblem; scenario = ()) prob.train_dataloader end +function get_hybridproblem_test_data(prob::HybridProblem; scenario = ()) + prob.test_data +end + function get_hybridproblem_cor_ends(prob::HybridProblem; scenario = ()) prob.cor_ends end diff --git a/src/HybridVariationalInference.jl b/src/HybridVariationalInference.jl index 035f750..e609b06 100644 --- a/src/HybridVariationalInference.jl +++ b/src/HybridVariationalInference.jl @@ -69,6 +69,7 @@ export AbstractHybridProblem, get_hybridproblem_MLapplicator, get_hybridproblem_ get_hybridproblem_float_type, gen_hybridproblem_synthetic, get_hybridproblem_par_templates, get_hybridproblem_transforms, get_hybridproblem_train_dataloader, + get_hybridproblem_test_data, get_hybridproblem_neg_logden_obs, get_hybridproblem_n_covar, get_hybridproblem_n_site_and_batch, diff --git a/src/elbo.jl b/src/elbo.jl index ff61483..039ea2b 100644 --- a/src/elbo.jl +++ b/src/elbo.jl @@ -594,12 +594,14 @@ end Extract correlation matrix of a problem based on `MeanHVIApproximation`. """ -function get_hybridproblem_correlation_Ms(prob::AbstractHybridProblem; scenario = Val(())) - UM = get_hybridproblem_cholesky_correlation_Ms(prob; scenario) +function get_hybridproblem_correlation_Ms(prob::AbstractHybridProblem; + xM = get_hyb, scenario = Val(())) + UM = get_hybridproblem_cholesky_correlation_Ms(prob; xM, scenario) UM' * UM end -function get_hybridproblem_cholesky_correlation_Ms(prob::AbstractHybridProblem; scenario = Val(())) +function get_hybridproblem_cholesky_correlation_Ms(prob::AbstractHybridProblem; + xM = nothing, scenario = Val(())) ϕq = get_hybridproblem_ϕq(prob; scenario) cor_ends = get_hybridproblem_cor_ends(prob; scenario) ρsM = ϕq[Val(:ρsM)] diff --git a/test/test_HybridProblem.jl b/test/test_HybridProblem.jl index 7505d41..d1387a3 100644 --- a/test/test_HybridProblem.jl +++ b/test/test_HybridProblem.jl @@ -50,9 +50,13 @@ function construct_problem(; scenario::Val{scen}) where scen rng = StableRNG(111) # n_batch = 10 n_site, n_batch = get_hybridproblem_n_site_and_batch(CP.DoubleMM.DoubleMMCase(); scenario) + n_site_test = 60 # dependency on DeoubleMMCase -> take care of changes in covariates - (; xM, θP_true, θMs_true, xP, y_true, y_o, y_unc - ) = gen_hybridproblem_synthetic(rng, DoubleMM.DoubleMMCase(); scenario) + (; xM, θP_true, θMs_true, xP, y_true, y_o, y_unc + ) = gen_hybridproblem_synthetic(rng, DoubleMM.DoubleMMCase(); n_site_test,scenario) + i_test = n_site .+ (1:n_site_test) + test_data = (; xM = xM[:, i_test], xP = xP[:, i_test], y_true = y_true[:, i_test], + y_o = y_o[:, i_test], y_unc = y_unc[:, i_test]) n_covar = size(xM,1) n_input = (:covarK2 ∈ scen) ? n_covar +1 : n_covar g_chain = SimpleChain( @@ -72,8 +76,10 @@ function construct_problem(; scenario::Val{scen}) where scen # MLUtils.DataLoader((xM, xP, y_o, y_unc, i_sites), batchsize=n_batch, partial=false) # end # end + i_train = 1:n_site train_dataloader = MLUtils.DataLoader( - (CA.getdata(xM), CA.getdata(xP), y_o, y_unc, i_sites), batchsize=n_batch, partial=false) + (CA.getdata(xM[:,i_train]), CA.getdata(xP[:,i_train]), y_o[:,i_train], + y_unc[:,i_train], i_sites[i_train]), batchsize=n_batch, partial=false) θall = vcat(θP, θM) priors_dict = Dict{Symbol, Distribution}( keys(θall) .=> fit.(LogNormal, θall, QuantilePoint.(θall .* 3, 0.95))) @@ -98,7 +104,7 @@ function construct_problem(; scenario::Val{scen}) where scen end HybridProblem(θM, ϕq, g_chain_scaled, ϕg0, f_batch, priors_dict, py, - transM, transP, train_dataloader, n_covar, n_site, n_batch, + transM, transP, train_dataloader, test_data, n_covar, n_site, n_batch, cor_ends, pbm_covars, approx, #ϕunc0, ) From fa51f5b3c48887a52a70d0d515c0d66cdeb6878a Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Sat, 21 Feb 2026 12:51:52 +0000 Subject: [PATCH 3/7] =?UTF-8?q?provide=20i=5Fsites=20to=20=20generate=5F?= =?UTF-8?q?=CE=B6=20and=20sample=5Fposterior?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/HVIApproximation.jl | 4 +++ src/HybridVariationalInference.jl | 1 + src/elbe_sepvec.jl | 49 +++++++++++++++++++++++++++++++ src/elbo.jl | 43 ++++++++++++++++++++------- src/elbo2.jl | 1 + test/test_elbo.jl | 16 +++++++--- test/test_sample_zeta.jl | 18 +++++++----- 7 files changed, 110 insertions(+), 22 deletions(-) create mode 100644 src/elbe_sepvec.jl diff --git a/src/HVIApproximation.jl b/src/HVIApproximation.jl index 7a6e45a..ceb29b0 100644 --- a/src/HVIApproximation.jl +++ b/src/HVIApproximation.jl @@ -18,3 +18,7 @@ struct MeanHVIApproximation <: AbstractMeanHVIApproximation end struct MeanHVIApproximationDev <: AbstractMeanHVIApproximation end +abstract type AbstractMeanVarSepHVIApproximation <: AbstractHVIApproximation end + +struct MeanVarSepHVIApproximation <: AbstractMeanVarSepHVIApproximation end + diff --git a/src/HybridVariationalInference.jl b/src/HybridVariationalInference.jl index e609b06..00ebc1f 100644 --- a/src/HybridVariationalInference.jl +++ b/src/HybridVariationalInference.jl @@ -42,6 +42,7 @@ include("bijectors_utils.jl") export AbstractHVIApproximation, AbstractMeanHVIApproximation export MeanHVIApproximation, MeanHVIApproximationMat +export AbstractMeanVarSepHVIApproximation, MeanVarSepHVIApproximation include("HVIApproximation.jl") export AbstractComponentArrayInterpreter, ComponentArrayInterpreter, diff --git a/src/elbe_sepvec.jl b/src/elbe_sepvec.jl new file mode 100644 index 0000000..37fd5f6 --- /dev/null +++ b/src/elbe_sepvec.jl @@ -0,0 +1,49 @@ +# Similar to MeanHVIApproximationVec +# but own variance parameter for each predicted parameter +function sample_ζresid_norm(approx::MeanVarSepHVIApproximation, + i_sites, + zP::AbstractMatrix, zMs::AbstractMatrix, + ϕm::TM, ϕq::AbstractVector{T}; + int_ϕq=get_concrete(ComponentArrayInterpreter(ϕq)), + cor_ends +) where {T,TM<:AbstractMatrix{T}} + ζMs = ϕm + ϕuncc = ϕqc = int_ϕq(CA.getdata(ϕq)) + ζP = ϕqc[Val(:μP)] + n_θP, n_θMs, (n_θM, n_batch) = length(ζP), length(ζMs), size(ζMs) + # do not create a UpperTriangular Matrix of an AbgeneraGÜUArray in transformU_cholesky1 + ρsP = isempty(ϕuncc[Val(:ρsP)]) ? similar(ϕuncc[Val(:ρsP)]) : ϕuncc[Val(:ρsP)] # required by zygote + UP = transformU_block_cholesky1(ρsP, cor_ends.P) + ρsM = isempty(ϕuncc[Val(:ρsM)]) ? similar(ϕuncc[Val(:ρsM)]) : ϕuncc[Val(:ρsM)] # required by zygote + # cholesky factor of the correlation: diag(UM' * UM) .== 1 + # coefficients ρsM can be larger than 1, still yielding correlations <1 in UM' * UM + UM = transformU_block_cholesky1(ρsM, cor_ends.M) + # + # make that ϕuncc[:logσ2_ζMs] stores the uncertainty for each site currently predicted + logσ2_logMs = ϕuncc[Val(:logσ2_ζMs)] + #logσ2_logMs = vec(cf[1, :] .+ cf[2, :] .* ζMs) + logσ2_ζP = vec(CA.getdata(ϕuncc[Val(:logσ2_ζP)])) + # CUDA cannot multiply BlockDiagonal * Diagonal, construct already those blocks + σMs = reshape(exp.(logσ2_logMs ./ 2), n_θM, :) + σP = exp.(logσ2_ζP ./ 2) + # BlockDiagonal does work with CUDA, but not with combination of Zygote and CUDA + # need to construct full matrix for CUDA + Uσ, diagUσ = _compute_choleskyfactor(UP, UM, σP, σMs, n_batch) # inferred only BlockDiagonal + #diagUσ = diag(Uσ)::typeof(σP) # elements of the diagonal: standard deviations + n_MC = size(zP, 1) + # is this multiplication efficient if Uσ is not concrete but only sumtype BlockDiagonal? + urandn = hcat(zP, zMs) + ζ_resids_parfirst = (Uσ' * urandn') #::typeof(urandn) # n_par x n_MC + #ζ_resids_parfirst = (urandn * Uσ)' #::typeof(urandn) # n_par x n_MC + #ζ_resids_parfirst = urandn' * Uσ # n_MC x n_par + # need to handle empty(ζP) explicitly, otherwise Zygote tries to take gradient + ζP_resids = isempty(ζP) ? ζ_resids_parfirst[1:0, :] : ζ_resids_parfirst[1:n_θP, :] + ζMs_parfirst_resids = reshape(ζ_resids_parfirst[(n_θP+1):end, :], n_θM, n_batch, n_MC) + ζP_resids, ζMs_parfirst_resids, diagUσ + # #map(std, eachcol(ζ_resids_parfirst[:, 3:8])) + # ζ_resid = transpose_mPMs_sitefirst(ζ_resids_parfirst; intm_PMs_parfirst) + # #map(std, eachcol(ζ_resid[:, 3:8])) # all ~ 0.1 in sample_ζresid_norm cpu + # #map(std, eachcol(ζ_resid[:, 2 + n_batch .+ (-1:5)])) # all ~ 100, except first two + # # returns AbstractGPUuArrays to either continue on GPU or need to transfer to CPU + # ζ_resid, diagUσ +end diff --git a/src/elbo.jl b/src/elbo.jl index 039ea2b..7e0ceeb 100644 --- a/src/elbo.jl +++ b/src/elbo.jl @@ -66,7 +66,7 @@ function neg_elbo_gtf_components(rng, ϕ::AbstractVector{FT}, g, f, py, ) where {FT} n_MCr = isempty(priors_θP_mean) ? n_MC : max(n_MC, n_MC_mean) ζsP, ζsMs, σ = generate_ζ(approx, rng, g, ϕ, xM; n_MC=n_MCr, cor_ends, pbm_covar_indices, - int_ϕq, int_ϕg_ϕq, is_testmode) + int_ϕq, int_ϕg_ϕq, is_testmode, i_sites) ζsP_cpu = cdev(ζsP) # fetch to CPU, because for <1000 sites (n_batch) this is faster ζsMs_cpu = cdev(ζsMs) # fetch to CPU, because for <1000 sites (n_batch) this is faster # @@ -310,22 +310,31 @@ Returns an NamedTuple `(; y, θsP, θsMs, entropy_ζ)` with entries that vary by site. - `entropy_ζ`: The entropy of the log-determinant of the transformation of the set of model parameters, which is involved in uncertainty quantification. + +Note that for some approximations, such as `MeanVarSepHVIApproximation`, +`prob.ϕq` contains uncertainty parameters that are specific to sites. +For a proper prediction for new sites, update `prob.ϕq` before appropriately. +If xM and xP is unspecified, the problem's sites are used and `prob.ϕq` is consistent. +If predicting for a subset of sites, specify keyword argument `i_sites` . """ function predict_hvi(rng, prob::AbstractHybridProblem; scenario=Val(()), gdevs = get_gdev_MP(scenario), xM = nothing, xP = nothing, is_testmode = true, + i_sites = nothing, kwargs... ) if isnothing(xM) || isnothing(xP) dl = get_hybridproblem_train_dataloader(prob; scenario) dl_dev = gdev_hybridproblem_dataloader(dl; gdevs) xM_dl, xP_dl = dl_dev.data[1:2] - xM = isnothing(xM) ? xM_dl : xM - xP = isnothing(xP) ? xP_dl : xP + i_sites = isnothing(i_sites) ? (1:size(xM_dl, 2)) : i_sites + xP = isnothing(xP) ? xP_dl[:,i_sites] : xP + xM = isnothing(xM) ? xM_dl[:,i_sites] : xM end + # sample_posterior required consistent prob.ϕq and xM (; θsP, θsMs, entropy_ζ) = sample_posterior( - rng, prob, xM; scenario, gdevs, is_testmode, kwargs...) + rng, prob, xM; scenario, gdevs, is_testmode, i_sites, kwargs...) # n_site, n_batch = get_hybridproblem_n_site_and_batch(prob; scenario) n_site_pred = size(θsMs,1) # determined by size(xM) @@ -369,13 +378,19 @@ Returns an NamedTuple `(; θsP, θsMs, entropy_ζ)` with entries - `entropy_ζ`: The entropy of the log-determinant of the transformation of the set of model parameters, which is involved in uncertainty quantification. """ -function sample_posterior(rng, prob::AbstractHybridProblem; scenario=Val(()), +function sample_posterior(rng, prob::AbstractHybridProblem; i_sites=nothing, scenario=Val(()), gdevs = get_gdev_MP(scenario), kwargs...) dl = get_hybridproblem_train_dataloader(prob; scenario) dl_dev = gdev_hybridproblem_dataloader(dl; gdevs) - xM = dl_dev.data[1] - sample_posterior(rng, prob, xM; scenario, gdevs, kwargs...) + xM_all = dl_dev.data[1] + if !isnothing(i_sites) + xM = xM_all[:,i_sites] + else + xM = xM_all + i_sites = 1:size(xM, 2) + end + sample_posterior(rng, prob, xM; scenario, gdevs, i_sites,kwargs...) end @@ -425,10 +440,11 @@ function sample_posterior(rng, g, ϕ::AbstractVector, xM::AbstractMatrix; is_inferred::Val{is_infer} = Val(false), is_testmode, approx::AbstractHVIApproximation, + i_sites, ) where is_infer ζsP_gpu, ζsMs_gpu, σ = generate_ζ(approx, rng, g, CA.getdata(ϕ), CA.getdata(xM); int_ϕg_ϕq, int_ϕq, - n_MC=n_sample_pred, cor_ends, pbm_covar_indices, is_testmode) + n_MC=n_sample_pred, cor_ends, pbm_covar_indices, is_testmode, i_sites) ζsP = cdev(ζsP_gpu) ζsMs = cdev(ζsMs_gpu) logdetΣ = 2 * sum(log.(σ)) @@ -459,6 +475,7 @@ function generate_ζ(approx::AbstractMeanHVIApproximation, rng::AbstractRNG, int_ϕq::AbstractComponentArrayInterpreter, n_MC=3, cor_ends, pbm_covar_indices, is_testmode, + i_sites, # = 1:size(xM,2), ) where {FT,MT} # see documentation of neg_elbo_gtf ϕc = int_ϕg_ϕq(CA.getdata(ϕ)) @@ -472,7 +489,8 @@ function generate_ζ(approx::AbstractMeanHVIApproximation, rng::AbstractRNG, xMP0 = _append_each_covars(xM, CA.getdata(μ_ζP), pbm_covar_indices) ϕm0 = g(xMP0, ϕg; is_testmode) μ_ζMs0 = ϕm0 - ζP_resids, ζMs_parfirst_resids, σ = sample_ζresid_norm(approx, rng, ϕm0, ϕq; n_MC, cor_ends, int_ϕq) + ζP_resids, ζMs_parfirst_resids, σ = sample_ζresid_norm(approx, rng, + i_sites, ϕm0, ϕq; n_MC, cor_ends, int_ϕq) ζsP = isempty(μ_ζP) ? ζP_resids : (μ_ζP .+ ζP_resids) # n_par x n_MC if pbm_covar_indices isa SA.SVector{0} # do not need to predict again but just add the residuals to μ_ζP and μ_ζMs @@ -568,6 +586,7 @@ ML-model predcitions of size `(n_θM, n_site)`. ρsP, ρsM, logσ2_ζP, coef_logσ2_ζMs(intercept + slope), """ function sample_ζresid_norm(approx::AbstractHVIApproximation, rng::Random.AbstractRNG, + i_sites, ϕm::AbstractMatrix, ϕq::AbstractVector, args...; n_MC, cor_ends, int_ϕq) @@ -583,7 +602,7 @@ function sample_ζresid_norm(approx::AbstractHVIApproximation, rng::Random.Abstr #z = _create_randn(rng, CA.getdata(ζP), n_MC, n_θP) zP = _create_randn(rng, CA.getdata(ζP), n_MC, n_θP) zMs = _create_randn(rng, CA.getdata(ζP), n_MC, n_θMs) - sample_ζresid_norm(approx, zP, zMs, CA.getdata(ϕm), ϕq, args...; + sample_ζresid_norm(approx, i_sites, zP, zMs, CA.getdata(ϕm), ϕq, args...; cor_ends, int_ϕq=get_concrete(int_ϕq) ) @@ -609,10 +628,12 @@ function get_hybridproblem_cholesky_correlation_Ms(prob::AbstractHybridProblem; end function sample_ζresid_norm(approx::MeanHVIApproximationMat, + i_sites, zP::AbstractMatrix, zMs::AbstractMatrix, ϕm::TM, ϕq::AbstractVector{T}; int_ϕq=get_concrete(ComponentArrayInterpreter(ϕq)), - cor_ends + cor_ends, + # assume to index into ϕq at the beginning, or provide those indices here ) where {T,TM<:AbstractMatrix{T}} ζMs = ϕm ϕuncc = ϕqc = int_ϕq(CA.getdata(ϕq)) diff --git a/src/elbo2.jl b/src/elbo2.jl index 7561159..c9a497e 100644 --- a/src/elbo2.jl +++ b/src/elbo2.jl @@ -16,6 +16,7 @@ # and forward runs for many sites (760mus cpu vs 439ms gpu unit!) function sample_ζresid_norm(app::MeanHVIApproximation, + i_sites, zP::AbstractMatrix, zMs::AbstractMatrix, ϕm::TM, ϕq::AbstractVector{T}; int_ϕq=get_concrete(ComponentArrayInterpreter(ϕq)), diff --git a/test/test_elbo.jl b/test/test_elbo.jl index 79d218f..30edee7 100644 --- a/test/test_elbo.jl +++ b/test/test_elbo.jl @@ -94,11 +94,13 @@ test_scenario = (scenario) -> begin g_gpu = ggdev(g_flux) end + i_sites = 1:n_batch ζsP, ζsMs, σ = @inferred ( # @descend_code_warntype ( CP.generate_ζ( - approx, rng, g, ϕ_ini, xM[:, 1:n_batch]; + approx, rng, g, ϕ_ini, xM[:, i_sites]; n_MC, cor_ends, pbm_covar_indices, + i_sites, int_ϕq=interpreters.ϕq, int_ϕg_ϕq=interpreters.ϕg_ϕq, is_testmode = false) ) @@ -114,7 +116,8 @@ test_scenario = (scenario) -> begin gr = Zygote.gradient( ϕ -> begin _ζsP, _ζsMs, _σ = CP.generate_ζ( - approx, rng, g, ϕ, xM[:, 1:n_batch]; + approx, rng, g, ϕ, xM[:, i_sites]; + i_sites, n_MC=8, cor_ends, pbm_covar_indices, int_ϕq=interpreters.ϕq, int_ϕg_ϕq=interpreters.ϕg_ϕq, is_testmode = true) @@ -155,11 +158,13 @@ test_scenario = (scenario) -> begin #hcat(ϕ_ini, ϕ, _ϕ)[1:4,:] #hcat(ϕ_ini, ϕ, _ϕ)[(end-20):end,:] n_predict = 10_000 #8_000 - xM_batch = xM[:, 1:n_batch] + i_sites = 1:n_batch + xM_batch = xM[:, i_sites] _ζsP, _ζsMs, _σ = @inferred ( # @descend_code_warntype ( CP.generate_ζ( approx, rng, g, _ϕ, xM_batch; + i_sites, n_MC = n_predict, cor_ends, pbm_covar_indices, int_ϕq=interpreters.ϕq, int_ϕg_ϕq=interpreters.ϕg_ϕq, is_testmode = true) @@ -241,11 +246,12 @@ test_scenario = (scenario) -> begin ϕ = ggdev(CA.getdata(ϕ_ini)) @test g_gpu.μ isa GPUArraysCore.AbstractGPUArray # @test g_gpu.app isa HybridVariationalInferenceFluxExt.FluxApplicator - xMg_batch = ggdev(xM[:, 1:n_batch]) + xMg_batch = ggdev(xM[:, i_sites]) ζsP_d, ζsMs_d, σ_d = @inferred ( # @descend_code_warntype ( CP.generate_ζ( approx, rng, g_gpu, ϕ, xMg_batch; + i_sites, n_MC, cor_ends, pbm_covar_indices, int_ϕq=interpreters.ϕq, int_ϕg_ϕq=interpreters.ϕg_ϕq, is_testmode = true)) @@ -260,6 +266,7 @@ test_scenario = (scenario) -> begin ϕ -> begin _ζsP, _ζsMs, _σ = CP.generate_ζ( approx, rng, g_gpu, ϕ, xMg_batch; + i_sites, n_MC, cor_ends, pbm_covar_indices, int_ϕq=interpreters.ϕq, int_ϕg_ϕq=interpreters.ϕg_ϕq, is_testmode = false) @@ -412,6 +419,7 @@ test_scenario = (scenario) -> begin #Cthulhu.@descend_code_warntype ( @inferred ( sample_posterior(rng, g, ϕ_ini, xM; + i_sites = 1:size(xM, 2), int_ϕg_ϕq, int_ϕq, transP, transM, cdev = identity, diff --git a/test/test_sample_zeta.jl b/test/test_sample_zeta.jl index 86991fc..1a4f26f 100644 --- a/test/test_sample_zeta.jl +++ b/test/test_sample_zeta.jl @@ -97,6 +97,7 @@ function test_with_scenario(scenario) int_ϕq = get_concrete(ComponentArrayInterpreter(ϕc.ϕq)) n_MC_pred = 300 # larger n_MC to test σ2 n_site_batch = size(ϕc.Ms,2) + i_sites = 1:n_site_batch #rng = StableRNG(111) # @inferred gives any, while Cthulhu inferres concrete type # ζP_resids, ζMs_parfirst_resids, σ = @inferred CP.sample_ζresid_norm(approx, rng, ϕc.Ms, ϕc.ϕq; @@ -105,7 +106,9 @@ function test_with_scenario(scenario) # n_MC=n_MC_pred, cor_ends, int_ϕq)) # ζP_resids, ζMs_parfirst_resids, σ = CP.sample_ζresid_norm(approx, rng, ϕc.P, ϕc.Ms, ϕc.ϕq; # n_MC=n_MC_pred, cor_ends, int_ϕq) - ζP_resids, ζMs_parfirst_resids, σ = @inferred CP.sample_ζresid_norm(approx, rng, ϕc.Ms, ϕc.ϕq; + i_sites = 1:n_site_batch + ζP_resids, ζMs_parfirst_resids, σ = @inferred CP.sample_ζresid_norm( + approx, rng, i_sites, ϕc.Ms, ϕc.ϕq; n_MC=n_MC_pred, cor_ends, int_ϕq) #@code_warntype CP.sample_ζresid_norm(approx, rng, ϕc.Ms, ϕc.ϕq; n_MC=n_MC_pred, cor_ends, int_ϕq) #@usingany Cthulhu @@ -117,7 +120,7 @@ function test_with_scenario(scenario) gr = Zygote.gradient(ϕc -> begin ζP_resids, ζMs_parfirst_resids, σ = CP.sample_ζresid_norm( - approx, rng, ϕc.Ms, ϕc.ϕq; + approx, rng, i_sites, ϕc.Ms, ϕc.ϕq; n_MC, cor_ends, int_ϕq) sum(ζP_resids) + sum(ζMs_parfirst_resids) end, ϕc)[1] @@ -149,8 +152,9 @@ function test_with_scenario(scenario) # ζP_resids, ζMs_parfirst_resids, σ = CP.sample_ζresid_norm( # approx, rng, CA.getdata(ϕcd.Ms), CA.getdata(ϕcd.ϕq); # n_MC = n_MC_pred, cor_ends, int_ϕq) + i_sites = 1:n_site_batch ζP_resids, ζMs_parfirst_resids, σ = @inferred CP.sample_ζresid_norm( - approx, rng, CA.getdata(ϕcd.Ms), CA.getdata(ϕcd.ϕq); + approx, rng, i_sites, CA.getdata(ϕcd.Ms), CA.getdata(ϕcd.ϕq); n_MC = n_MC_pred, cor_ends, int_ϕq) #@descend_code_warntype CP.sample_ζresid_norm(rng, CA.getdata(ϕcd.Ms), CA.getdata(ϕcd.ϕq); n_MC = n_MC_pred, cor_ends, int_ϕq) @test ζP_resids isa GPUArraysCore.AbstractGPUArray @@ -180,26 +184,26 @@ function test_with_scenario(scenario) () -> begin CP.sample_ζresid_norm( #@benchmark CP.sample_ζresid_norm( - approx, rng, ϕc.Ms, ϕc.ϕq; + approx, rng, i_sites, ϕc.Ms, ϕc.ϕq; n_MC, cor_ends, int_ϕq) # CP.sample_ζresid_norm( #@benchmark CP.sample_ζresid_norm( - approx, rng, ϕcd.Ms, ϕcd.ϕq; + approx, rng, i_sites, ϕcd.Ms, ϕcd.ϕq; n_MC, cor_ends, int_ϕq) # ϕc_few = CA.ComponentVector(ϕc; Ms = ϕc.Ms[:,1:n_site_few]); Zygote.gradient(ϕc -> begin #@benchmark Zygote.gradient(ϕc -> begin # many small allocs ζP_resids, ζMs_parfirst_resids, σ = CP.sample_ζresid_norm( - approx, rng, ϕc.Ms, ϕc.ϕq; + approx, rng, i_sites, ϕc.Ms, ϕc.ϕq; n_MC, cor_ends, int_ϕq) sum(ζP_resids) + sum(ζMs_parfirst_resids) end, ϕc_few)[1] Zygote.gradient(ϕc -> begin # many small allocs #@benchmark Zygote.gradient(ϕc -> begin # many small allocs ζP_resids, ζMs_parfirst_resids, σ = CP.sample_ζresid_norm( - approx, rng, CA.getdata(ϕc.Ms), CA.getdata(ϕc.ϕq); + approx, rng, i_sites, CA.getdata(ϕc.Ms), CA.getdata(ϕc.ϕq); n_MC, cor_ends, int_ϕq) sum(ζP_resids) + sum(ζMs_parfirst_resids) end, ϕcd_few)[1] From 7ba70b38ebe876ad0258fa02590448348f7b686c Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Sat, 21 Feb 2026 20:02:08 +0000 Subject: [PATCH 4/7] first implementation of MeanVarSepHVIApproximation where a separte vector of variances is estimated for each site. Need to pass n_site and thetaM to initialization of initial parameter vector --- docs/src/tutorials/basic_cpu.md | 5 +-- docs/src/tutorials/basic_cpu.qmd | 10 ++---- .../intermediate/basic_cpu_results.jld2 | Bin 196159 -> 196159 bytes src/DoubleMM/f_doubleMM.jl | 7 ++-- src/HybridProblem.jl | 21 ++++++++---- src/HybridVariationalInference.jl | 3 +- src/elbo.jl | 4 ++- src/elbo2.jl | 4 +-- src/{elbe_sepvec.jl => elbo_sepvec.jl} | 6 ++-- src/init_hybrid_params.jl | 28 +++++++++++++++ test/test_HybridProblem.jl | 4 +-- test/test_elbo.jl | 15 ++++---- test/test_sample_zeta.jl | 32 +++++++++++++++--- 13 files changed, 101 insertions(+), 38 deletions(-) rename src/{elbe_sepvec.jl => elbo_sepvec.jl} (95%) diff --git a/docs/src/tutorials/basic_cpu.md b/docs/src/tutorials/basic_cpu.md index a425e56..ce6252c 100644 --- a/docs/src/tutorials/basic_cpu.md +++ b/docs/src/tutorials/basic_cpu.md @@ -242,12 +242,13 @@ given a vector of global parameters, and a matrix of site parameters to invocation of the process based model (PBM), defined at the beginning. ``` julia +approx = MeanHVIApproximation() f_batch = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1]) -ϕq0 = init_hybrid_ϕq(MeanHVIApproximation(), θP, θM, transP) +ϕq0 = init_hybrid_ϕq(approx, θP, θM, transP; n_site) prob = HybridProblem(θM, ϕq0, g_chain_scaled, ϕg0, f_batch, priors_dict, py, - transM, transP, train_dataloader, test_data,n_covar, n_site, n_batch) + transM, transP, train_dataloader, test_data,n_covar, n_site, n_batch; approx) ``` ## Perform the inversion diff --git a/docs/src/tutorials/basic_cpu.qmd b/docs/src/tutorials/basic_cpu.qmd index b347a43..77621c1 100644 --- a/docs/src/tutorials/basic_cpu.qmd +++ b/docs/src/tutorials/basic_cpu.qmd @@ -18,11 +18,6 @@ bibliography: twutz_txt.bib CurrentModule = HybridVariationalInference ``` -Julias version: -```{julia} -VERSION -``` - First load necessary packages. ```{julia} using HybridVariationalInference @@ -253,12 +248,13 @@ given a vector of global parameters, and a matrix of site parameters to invocation of the process based model (PBM), defined at the beginning. ```{julia} +approx = MeanHVIApproximation() f_batch = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1]) -ϕq0 = init_hybrid_ϕq(MeanHVIApproximation(), θP, θM, transP) +ϕq0 = init_hybrid_ϕq(approx, θP, θM, transP; n_site) prob = HybridProblem(θM, ϕq0, g_chain_scaled, ϕg0, f_batch, priors_dict, py, - transM, transP, train_dataloader, test_data,n_covar, n_site, n_batch) + transM, transP, train_dataloader, test_data,n_covar, n_site, n_batch; approx) ``` ```{julia} diff --git a/docs/src/tutorials/intermediate/basic_cpu_results.jld2 b/docs/src/tutorials/intermediate/basic_cpu_results.jld2 index 31d167c1f2efc6ced0d239dfea44ad3ffb856e9a..1cc54880e4bd2f9584433be80ceca2b987e840ca 100644 GIT binary patch delta 75 zcmV-R0JQ(V{0qPQ3$Td90WXu01R|5G#1sMmNt3Y#AG7Ghumb{U1GAyXUjva~4}#2w h%mKE{0{bBWFPHr70zw3%DZCb!vGf8Pw_WZ6;t%nEAnyPG delta 73 zcmdo0hkO4Y?hPGB7>y=RWKo*D`iKaJZ(<1p2yEVe^r7M`~ZagA4&iK diff --git a/src/DoubleMM/f_doubleMM.jl b/src/DoubleMM/f_doubleMM.jl index 20818cf..dd2206a 100644 --- a/src/DoubleMM/f_doubleMM.jl +++ b/src/DoubleMM/f_doubleMM.jl @@ -452,10 +452,13 @@ function HVI.get_hybridproblem_cor_ends(prob::DoubleMMCase; scenario::Val{scen}) end end -function HVI.get_hybridproblem_ϕq(prob::DoubleMMCase; scenario) +function HVI.get_hybridproblem_ϕq(prob::DoubleMMCase; scenario::Val{scen}) where {scen} + approx = (:sepvar ∈ scen) ? MeanVarSepHVIApproximation() : MeanHVIApproximationMat() FT = get_hybridproblem_float_type(prob; scenario) cor_ends = get_hybridproblem_cor_ends(prob; scenario) - ϕunc = init_hybrid_ϕunc(MeanHVIApproximationMat(), cor_ends, zero(FT)) + θM, θP = get_hybridproblem_par_templates(prob; scenario) + n_site, _ = get_hybridproblem_n_site_and_batch(prob; scenario) + ϕunc = init_hybrid_ϕunc(approx, cor_ends, zero(FT); θM, n_site) # for DoubleMMCase templates gives the correct values θP = get_hybridproblem_par_templates(prob; scenario).θP transP = get_hybridproblem_transforms(prob; scenario).transP diff --git a/src/HybridProblem.jl b/src/HybridProblem.jl index 6c75001..3ba7a85 100644 --- a/src/HybridProblem.jl +++ b/src/HybridProblem.jl @@ -21,7 +21,10 @@ Fields: defaults to `(P = [length(θP)], M = [length(θM)])` - `pbm_covars::NTuple{N,Symbol}`: names of global parameters used as covariates in the ML model, defaults to `()`, i.e. no covariates fed into the ML model - +- `approx::AbstractHVIApproximation`: the approximation to use for the variational + distribution, defaults to `MeanHVIApproximationMat()`, i.e. correlation matrix + shared across all sites and variance parameters a function of the predicted + parameters. """ struct HybridProblem <: AbstractHybridProblem #θP::CA.ComponentVector @@ -57,7 +60,7 @@ struct HybridProblem <: AbstractHybridProblem test_data::NamedTuple, n_covar::Int, n_site::Int, - n_batch::Int, + n_batch::Int; cor_ends::NamedTuple = (P = [length(ϕq[Val(:μP)])], M = [length(θM)]), pbm_covars::NTuple{N,Symbol} = (), approx::AbstractHVIApproximation = MeanHVIApproximationMat() @@ -69,22 +72,26 @@ struct HybridProblem <: AbstractHybridProblem end """ - init_hybrid_ϕq(θP, θM, transP; cor_ends) + init_hybrid_ϕq(θP, θM, transP; cor_ends, n_site) Initialize the non-ML parameter vector. """ function init_hybrid_ϕq( - approx::AbstractMeanHVIApproximation, + approx::Union{AbstractMeanHVIApproximation, AbstractMeanVarSepHVIApproximation}, θP::CA.ComponentVector, θM::CA.ComponentVector, transP::Stacked, - cor_ends::NamedTuple = (P = [length(θP)], M = [length(θM)]), + cor_ends::NamedTuple = (P = [length(θP)], M = [length(θM)]); + n_site::Integer, ) FT = promote_type(eltype(θP), eltype(θM)) - ϕunc0 = init_hybrid_ϕunc(approx, cor_ends, zero(FT)) + ϕunc0 = init_hybrid_ϕunc(approx, cor_ends, zero(FT); θM, n_site) ϕq = update_μP_by_θP(ϕunc0, θP, transP) end + + + """ create_ϕq(θP, ϕunc, transP::Stacked) @@ -140,7 +147,7 @@ function update_hybridProblem(prob::AbstractHybridProblem; scenario, ϕq = CA.ComponentVector(ϕq; ϕunc...) end HybridProblem(θM, ϕq, g, ϕg, f_batch, priors, py, transM, transP, train_dataloader, - test_data, n_covar, n_site, n_batch, cor_ends_new, pbm_covars, approx) + test_data, n_covar, n_site, n_batch; cor_ends = cor_ends_new, pbm_covars, approx) end function HybridProblem(prob::HybridProblem; kwargs... ) diff --git a/src/HybridVariationalInference.jl b/src/HybridVariationalInference.jl index 00ebc1f..2602191 100644 --- a/src/HybridVariationalInference.jl +++ b/src/HybridVariationalInference.jl @@ -82,7 +82,7 @@ export AbstractHybridProblem, get_hybridproblem_MLapplicator, get_hybridproblem_ gdev_hybridproblem_dataloader, gdev_hybridproblem_data, setup_PBMpar_interpreter, get_gdev_MP, - init_hybrid_ϕq + init_hybrid_ϕq include("AbstractHybridProblem.jl") export HybridProblem @@ -114,6 +114,7 @@ include("cholesky.jl") export neg_elbo_gtf, sample_posterior, predict_hvi, zero_penalty_loss export get_hybridproblem_correlation_Ms include("elbo_dev.jl") +include("elbo_sepvec.jl") include("elbo.jl") include("elbo2.jl") diff --git a/src/elbo.jl b/src/elbo.jl index 7e0ceeb..45250ff 100644 --- a/src/elbo.jl +++ b/src/elbo.jl @@ -469,7 +469,9 @@ model. The output shape of size `(n_site x n_par x n_MC)` is tailored to iterating each MC sample and then transforming each parameter on block across sites. """ -function generate_ζ(approx::AbstractMeanHVIApproximation, rng::AbstractRNG, +function generate_ζ( + approx::Union{AbstractMeanHVIApproximation, AbstractMeanVarSepHVIApproximation}, + rng::AbstractRNG, g, ϕ::AbstractVector{FT}, xM::MT; int_ϕg_ϕq::AbstractComponentArrayInterpreter, int_ϕq::AbstractComponentArrayInterpreter, diff --git a/src/elbo2.jl b/src/elbo2.jl index c9a497e..f6b477c 100644 --- a/src/elbo2.jl +++ b/src/elbo2.jl @@ -1,4 +1,4 @@ -# Re-Implementation of MeanHVIApproximationVec where random numbers are +# Re-Implementation of MeanHVIApproximationMat where random numbers are # generated for each block in the correlation matrix separately. # # Unfortunately, having many smaller matrix multiplications, the derivative @@ -11,7 +11,7 @@ # this implementation is faster on gpu, but slower than the Vec # implementation on CPU (that uses triangular and blocked matrices) # -# Hence, currently, rather use the MeanHVIApproximationVec CPU implementation. +# Hence, currently, rather use the MeanHVIApproximationMat CPU implementation. # for gradient on few sites (1.6ms cpu vs 5.4ms gpu) # and forward runs for many sites (760mus cpu vs 439ms gpu unit!) diff --git a/src/elbe_sepvec.jl b/src/elbo_sepvec.jl similarity index 95% rename from src/elbe_sepvec.jl rename to src/elbo_sepvec.jl index 37fd5f6..80ce903 100644 --- a/src/elbe_sepvec.jl +++ b/src/elbo_sepvec.jl @@ -1,4 +1,4 @@ -# Similar to MeanHVIApproximationVec +# Similar to MeanHVIApproximationMat # but own variance parameter for each predicted parameter function sample_ζresid_norm(approx::MeanVarSepHVIApproximation, i_sites, @@ -20,8 +20,7 @@ function sample_ζresid_norm(approx::MeanVarSepHVIApproximation, UM = transformU_block_cholesky1(ρsM, cor_ends.M) # # make that ϕuncc[:logσ2_ζMs] stores the uncertainty for each site currently predicted - logσ2_logMs = ϕuncc[Val(:logσ2_ζMs)] - #logσ2_logMs = vec(cf[1, :] .+ cf[2, :] .* ζMs) + logσ2_logMs = ϕuncc[Val(:logσ2_ζMs)][:,i_sites] logσ2_ζP = vec(CA.getdata(ϕuncc[Val(:logσ2_ζP)])) # CUDA cannot multiply BlockDiagonal * Diagonal, construct already those blocks σMs = reshape(exp.(logσ2_logMs ./ 2), n_θM, :) @@ -47,3 +46,4 @@ function sample_ζresid_norm(approx::MeanVarSepHVIApproximation, # # returns AbstractGPUuArrays to either continue on GPU or need to transfer to CPU # ζ_resid, diagUσ end + diff --git a/src/init_hybrid_params.jl b/src/init_hybrid_params.jl index bd9473b..0277f83 100644 --- a/src/init_hybrid_params.jl +++ b/src/init_hybrid_params.jl @@ -112,6 +112,8 @@ function init_hybrid_ϕunc( hcat, (coef_logσ2_logM for _ in 1:cor_ends.M[end])), ρsP = fill(ρ0, get_cor_count(cor_ends.P)), ρsM = fill(ρ0, get_cor_count(cor_ends.M)), + θM::CA.ComponentVector, + n_site::Integer = 0, ) where {FT} nt = (; logσ2_ζP, @@ -121,6 +123,32 @@ function init_hybrid_ϕunc( ca = CA.ComponentVector(;nt...)::CA.ComponentVector end +function init_hybrid_ϕunc( + approx::AbstractMeanVarSepHVIApproximation, + cor_ends::NamedTuple, + ρ0::FT = 0.0f0, + logσ2_ζMs::AbstractMatrix{FT} = Array{FT}(undef, 0, 0), + logσ2_ζP::AbstractVector{FT} = fill(FT(-10.0), cor_ends.P[end]), + ρsP = fill(ρ0, get_cor_count(cor_ends.P)), + ρsM = fill(ρ0, get_cor_count(cor_ends.M)); + θM::CA.ComponentVector, + n_site::Integer, + relerr = 0.01, +) where {FT} + logσ2_ζMs = if isempty(logσ2_ζMs) + # sigma is the relative error of the template of θM + repeat(FT(2) * log.(convert(FT,relerr) .* CA.getdata(θM)), 1, n_site) + else + logσ2_ζMs + end + nt = (; + logσ2_ζP, + logσ2_ζMs, + ρsP, + ρsM) + ca = CA.ComponentVector(;nt...)::CA.ComponentVector +end + # macro gen_unc(nt) # quote # nt_ev = $(esc(nt)) diff --git a/test/test_HybridProblem.jl b/test/test_HybridProblem.jl index d1387a3..ecbbfe8 100644 --- a/test/test_HybridProblem.jl +++ b/test/test_HybridProblem.jl @@ -95,7 +95,7 @@ function construct_problem(; scenario::Val{scen}) where scen f_batch = PBMSiteApplicator( f_doubleMM; θP, θM, θFix=CA.ComponentVector{FT}(), xPvec=xP[:,1]) - ϕunc0 = init_hybrid_ϕunc(MeanHVIApproximation(), cor_ends, zero(FT)) + ϕunc0 = init_hybrid_ϕunc(MeanHVIApproximation(), cor_ends, zero(FT); θM, n_site) ϕq = CP.update_μP_by_θP(ϕunc0, θP, transP) approx = if (:MeanHVIApproxBlocks ∈ scen) MeanHVIApproximation() @@ -104,7 +104,7 @@ function construct_problem(; scenario::Val{scen}) where scen end HybridProblem(θM, ϕq, g_chain_scaled, ϕg0, f_batch, priors_dict, py, - transM, transP, train_dataloader, test_data, n_covar, n_site, n_batch, + transM, transP, train_dataloader, test_data, n_covar, n_site, n_batch; cor_ends, pbm_covars, approx, #ϕunc0, ) diff --git a/test/test_elbo.jl b/test/test_elbo.jl index 30edee7..58c6d7c 100644 --- a/test/test_elbo.jl +++ b/test/test_elbo.jl @@ -28,10 +28,10 @@ const prob = DoubleMM.DoubleMMCase() scenario = Val((:default,)) #scenario = Val((:covarK2,)) -const approx = MeanHVIApproximationMat() -#const approx = MeanHVIApproximation() +#approx = MeanHVIApproximationMat() +#approx = MeanVarSepHVIApproximation() -test_scenario = (scenario) -> begin +test_scenario = (scenario, approx) -> begin probc = HybridProblem(prob; scenario, approx); FT = get_hybridproblem_float_type(probc; scenario) par_templates = get_hybridproblem_par_templates(probc; scenario) @@ -74,7 +74,7 @@ test_scenario = (scenario) -> begin # transP = elementwise(exp) # transM = Stacked(elementwise(identity), elementwise(exp)) #transM = Stacked(elementwise(identity), elementwise(exp), elementwise(exp)) # test mismatch - ϕq0 = init_hybrid_ϕq(approx, par_templates.θP, par_templates.θM, transP, cor_ends) + ϕq0 = init_hybrid_ϕq(approx, par_templates.θP, par_templates.θM, transP, cor_ends; n_site) # ϕunc0 = init_hybrid_ϕunc(cor_ends, zero(FT)) # ϕq0 = CP.update_μP_by_θP(ϕunc0, θP_true, transP) (; ϕ, interpreters) = init_hybrid_params(ϕg0, ϕq0) @@ -126,7 +126,7 @@ test_scenario = (scenario) -> begin @test gr[1] isa Vector end - if !(:covarK2 ∈ CP._val_value(scenario)) + if !(:covarK2 ∈ CP._val_value(scenario)) && (approx isa MeanHVIApproximation) # can only test distribution if g is not repeated @testset "generate_ζ check sd residuals $(last(CP._val_value(scenario)))" begin # prescribe very different uncertainties @@ -498,7 +498,8 @@ test_scenario = (scenario) -> begin end # test_scenario -test_scenario(Val((:default,))) +test_scenario(Val((:default,)), MeanHVIApproximationMat()) +test_scenario(Val((:default,)), MeanVarSepHVIApproximation()) # with providing process parameter as additional covariate -test_scenario(Val((:covarK2,))) +test_scenario(Val((:covarK2,)), MeanHVIApproximationMat()) diff --git a/test/test_sample_zeta.jl b/test/test_sample_zeta.jl index 1a4f26f..2c27982 100644 --- a/test/test_sample_zeta.jl +++ b/test/test_sample_zeta.jl @@ -55,6 +55,10 @@ function test_with_scenario(scenario) Ms=θMs_true) ϕ_true = CA.ComponentVector(Ms = θMs_true, ϕq=ϕq_true) ϕ_cpu = CA.ComponentVector(Ms = θMs_true .+ FT(0.01), ϕq=ϕq) + # initial estimate of error in θMs of 2% of true value + ϕ_cpu_sepvar = CA.ComponentVector(Ms = θMs_true, + ϕq = CA.ComponentVector(ϕ_cpu.ϕq; + logσ2_ζMs = 2 .* log.(0.02 * θMs_true .+ FT(0.01)))) interpreters = (; pmu=ComponentArrayInterpreter(ϕ_true), ϕq=ComponentArrayInterpreter(ϕq) @@ -90,8 +94,9 @@ function test_with_scenario(scenario) # approx = MeanHVIApproximation() # approx = MeanHVIApproximationMat() # approx = CP.MeanHVIApproximationDev() + # approx = MeanVarSepHVIApproximation() function test_sample_ζresid_norm(approx) - ϕc = copy(ϕ_cpu) + ϕc = approx isa MeanVarSepHVIApproximation ? copy(ϕ_cpu_sepvar) : copy(ϕ_cpu) ϕc.ϕq.coef_logσ2_ζMs[1,:] .= (log ∘ abs2).((0.1, 100.0)) ϕc.ϕq.ρsM .= 0.0 int_ϕq = get_concrete(ComponentArrayInterpreter(ϕc.ϕq)) @@ -133,9 +138,16 @@ function test_with_scenario(scenario) # isapprox(std(xc.Ms[:,1,1]), 0.1, rtol = 0.1) # site 1 parameter 1 # isapprox(std(xc.Ms[:,:,1]), 0.1, rtol = 0.1) # parameter 1 # isapprox(std(xc.Ms[:,:,2]), 100.1, rtol = 0.1) # parameter 2 - isapprox(std(ζMs_parfirst_resids[1,1,:]), 0.1, rtol = 0.1) # site 1 parameter 1 - isapprox(std(ζMs_parfirst_resids[1,:,:]), 0.1, rtol = 0.1) # parameter 1 - isapprox(std(ζMs_parfirst_resids[2,:,:]), 100.1, rtol = 0.1) # parameter 2 + if approx isa MeanVarSepHVIApproximation + isapprox(std(ζMs_parfirst_resids[1,1,:]), exp(ϕc.ϕq.logσ2_ζMs[1,1]/2), rtol = 0.1) # site 1 parameter 1 + isapprox(std(ζMs_parfirst_resids[1,end,:]), exp(ϕc.ϕq.logσ2_ζMs[1,end]/2), rtol = 0.1) # site 1 parameter 1 + isapprox(std(ζMs_parfirst_resids[end,1,:]), exp(ϕc.ϕq.logσ2_ζMs[end,1]/2), rtol = 0.1) # site 1 parameter 1 + isapprox(std(ζMs_parfirst_resids[end,end,:]), exp(ϕc.ϕq.logσ2_ζMs[end,end]/2), rtol = 0.1) # site 1 parameter 1 + else + isapprox(std(ζMs_parfirst_resids[1,1,:]), 0.1, rtol = 0.1) # site 1 parameter 1 + isapprox(std(ζMs_parfirst_resids[1,:,:]), 0.1, rtol = 0.1) # parameter 1 + isapprox(std(ζMs_parfirst_resids[2,:,:]), 100.1, rtol = 0.1) # parameter 2 + end # if ggdev isa MLDataDevices.AbstractGPUDevice @testset "sample_ζresid_norm gpu" begin @@ -223,6 +235,12 @@ function test_with_scenario(scenario) approx = MeanHVIApproximation() test_sample_ζresid_norm(approx) end + + @testset "sample_ζresid_norm MeanVarSepHVIApproximation $(last(CP._val_value(scenario)))" begin + approx = MeanVarSepHVIApproximation() + test_sample_ζresid_norm(approx) + end + end @testset "default scenario" begin @@ -230,6 +248,12 @@ end test_with_scenario(scenario) end +@testset "sepvar" begin + scenario = Val((:sepvar,)) + test_with_scenario(scenario) +end + + @testset "noglobals scenario" begin scenario = Val((:no_globals,)) test_with_scenario(scenario) From 831581aa50b93bc24d4b86367ba509423d4390e3 Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Thu, 26 Feb 2026 13:38:05 +0000 Subject: [PATCH 5/7] estimate sigma given Stacked Bijector --- src/HybridProblem.jl | 5 +++-- src/elbo.jl | 3 ++- src/init_hybrid_params.jl | 19 ++++++++++++++++++- test/runtests.jl | 2 ++ test/test_init_unc.jl | 29 +++++++++++++++++++++++++++++ 5 files changed, 54 insertions(+), 4 deletions(-) create mode 100644 test/test_init_unc.jl diff --git a/src/HybridProblem.jl b/src/HybridProblem.jl index 3ba7a85..32fb5b5 100644 --- a/src/HybridProblem.jl +++ b/src/HybridProblem.jl @@ -83,9 +83,10 @@ function init_hybrid_ϕq( transP::Stacked, cor_ends::NamedTuple = (P = [length(θP)], M = [length(θM)]); n_site::Integer, + kwargs..., ) FT = promote_type(eltype(θP), eltype(θM)) - ϕunc0 = init_hybrid_ϕunc(approx, cor_ends, zero(FT); θM, n_site) + ϕunc0 = init_hybrid_ϕunc(approx, cor_ends, zero(FT); θM, n_site, kwargs...) ϕq = update_μP_by_θP(ϕunc0, θP, transP) end @@ -151,7 +152,7 @@ function update_hybridProblem(prob::AbstractHybridProblem; scenario, end function HybridProblem(prob::HybridProblem; kwargs... ) - update_hybridProblem(prob; scenario = Val(()), kwargs..., approx = prob.approx) + update_hybridProblem(prob; scenario = Val(()), approx = prob.approx, kwargs...) end diff --git a/src/elbo.jl b/src/elbo.jl index 45250ff..8239081 100644 --- a/src/elbo.jl +++ b/src/elbo.jl @@ -614,9 +614,10 @@ end get_hybridproblem_correlation_Ms(prob; scenario = Val(())) Extract correlation matrix of a problem based on `MeanHVIApproximation`. +At unconstrained parameter scale. """ function get_hybridproblem_correlation_Ms(prob::AbstractHybridProblem; - xM = get_hyb, scenario = Val(())) + xM = nothing, scenario = Val(())) UM = get_hybridproblem_cholesky_correlation_Ms(prob; xM, scenario) UM' * UM end diff --git a/src/init_hybrid_params.jl b/src/init_hybrid_params.jl index 0277f83..efe810c 100644 --- a/src/init_hybrid_params.jl +++ b/src/init_hybrid_params.jl @@ -131,13 +131,15 @@ function init_hybrid_ϕunc( logσ2_ζP::AbstractVector{FT} = fill(FT(-10.0), cor_ends.P[end]), ρsP = fill(ρ0, get_cor_count(cor_ends.P)), ρsM = fill(ρ0, get_cor_count(cor_ends.M)); + transM, θM::CA.ComponentVector, n_site::Integer, relerr = 0.01, ) where {FT} logσ2_ζMs = if isempty(logσ2_ζMs) # sigma is the relative error of the template of θM - repeat(FT(2) * log.(convert(FT,relerr) .* CA.getdata(θM)), 1, n_site) + σ = compute_σ_unconstrained(transM, CA.getdata(θM), relerr) + repeat(FT(2) * log.(convert.(FT,σ)), 1, n_site) else logσ2_ζMs end @@ -149,6 +151,21 @@ function init_hybrid_ϕunc( ca = CA.ComponentVector(;nt...)::CA.ComponentVector end +function compute_σ_unconstrained(transM::Stacked, θM, rel_err) + σ = mapreduce(vcat, transM.bs, transM.ranges_in) do b, range_in + θM_sub = θM[range_in] + #b, θM_sub + compute_σ_unconstrained(b, θM_sub, rel_err) + end +end +function compute_σ_unconstrained(::HybridVariationalInference.Exp, θM::AbstractArray{T}, rel_err) where T + σ_single = sqrt.(log.(abs2(convert(T,rel_err)) .+ one(T))) # Wutzler 2020 + fill(σ_single, size(θM)) +end +function compute_σ_unconstrained(::typeof(identity), θM::AbstractArray{T}, rel_err) where T + convert(T,rel_err) .* θM +end + # macro gen_unc(nt) # quote # nt_ev = $(esc(nt)) diff --git a/test/runtests.jl b/test/runtests.jl index 0d110ed..41e0902 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -31,6 +31,8 @@ const GROUP = get(ENV, "GROUP", "All") # defined in in CI.yml @time @safetestset "test_cholesky_structure" include("test_cholesky_structure.jl") #@safetestset "test" include("test/test_sample_zeta.jl") @time @safetestset "test_sample_zeta" include("test_sample_zeta.jl") + #@safetestset "test" include("test/test_init_unc.jl") + @time @safetestset "test_elbo" include("test_init_unc.jl") #@safetestset "test" include("test/test_elbo.jl") @time @safetestset "test_elbo" include("test_elbo.jl") # diff --git a/test/test_init_unc.jl b/test/test_init_unc.jl new file mode 100644 index 0000000..1f063f0 --- /dev/null +++ b/test/test_init_unc.jl @@ -0,0 +1,29 @@ +#using LinearAlgebra, BlockDiagonals +using LinearAlgebra + +using Test +using HybridVariationalInference +using HybridVariationalInference: HybridVariationalInference as CP +using HybridVariationalInference: HybridVariationalInference as HVI +using StableRNGs +using Random +using ComponentArrays: ComponentArrays as CA +using Bijectors + +# using Zygote +# import GPUArraysCore: GPUArraysCore +# #import CUDA, cuDNN +# using MLDataDevices + +@testset "compute_σ_unconstrained" begin + FT = Float32 + ζM = rand(FT, 5) + rel_err = 0.1 + transM = Stacked((HVI.Exp(),identity),(1:2,3:5)) + θM = transM(ζM) + σ = @inferred CP.compute_σ_unconstrained(transM, θM, rel_err) + @test σ[1] == σ[2] ≈ FT(sqrt(log(rel_err^2 + 1.0))) # exp only depends on rel_err + @test all(σ[3:5] .≈ FT(rel_err) .* θM[3:5]) + @test eltype(σ) == eltype(θM) +end + From 82e832cc60137512272e13443192f43f0dca9d99 Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Fri, 20 Mar 2026 14:22:03 +0000 Subject: [PATCH 6/7] =?UTF-8?q?rename=20=CE=B8sMs=20to=20=CE=B8sMs=5Ftr?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit to reflect the shape with sites in columns rahter than in last dimension --- docs/src/tutorials/basic_cpu.md | 26 ++--- docs/src/tutorials/basic_cpu.qmd | 20 ++-- docs/src/tutorials/blocks_corr.md | 14 +-- docs/src/tutorials/blocks_corr.qmd | 14 +-- .../figure-commonmark/cell-10-output-1.png | Bin 102932 -> 97959 bytes .../figure-commonmark/cell-11-output-1.png | Bin 105140 -> 105495 bytes .../figure-commonmark/cell-12-output-1.png | Bin 61327 -> 68473 bytes docs/src/tutorials/corr_site_global.md | 4 +- docs/src/tutorials/corr_site_global.qmd | 4 +- .../figure-commonmark/cell-10-output-1.png | Bin 110309 -> 109948 bytes .../figure-commonmark/cell-11-output-1.png | Bin 148848 -> 144181 bytes .../figure-commonmark/cell-12-output-1.png | Bin 131474 -> 128016 bytes .../figure-commonmark/cell-9-output-1.png | Bin 97193 -> 89634 bytes docs/src/tutorials/inspect_results.md | 20 ++-- docs/src/tutorials/inspect_results.qmd | 20 ++-- .../figure-commonmark/cell-10-output-1.png | Bin 93015 -> 89919 bytes .../figure-commonmark/cell-13-output-1.png | Bin 68022 -> 74984 bytes .../figure-commonmark/cell-8-output-1.png | Bin 89084 -> 86162 bytes .../intermediate/basic_cpu_results.jld2 | Bin 196159 -> 193191 bytes docs/src/tutorials/logden_user.md | 4 +- docs/src/tutorials/logden_user.qmd | 4 +- .../figure-commonmark/cell-10-output-1.png | Bin 96342 -> 90152 bytes .../figure-commonmark/cell-11-output-1.png | Bin 108300 -> 122221 bytes .../figure-commonmark/cell-12-output-1.png | Bin 138175 -> 134946 bytes .../figure-commonmark/cell-8-output-1.png | Bin 31446 -> 31871 bytes .../figure-commonmark/cell-9-output-1.png | Bin 29724 -> 31074 bytes docs/src/tutorials/lux_gpu.md | 6 +- docs/src/tutorials/lux_gpu.qmd | 4 +- projects/doubleMM.jl | 110 +++++++++--------- src/DoubleMM/f_doubleMM.jl | 95 ++++----------- src/HybridProblem.jl | 10 +- src/HybridSolver.jl | 10 +- src/PBMApplicator.jl | 99 ++++++++-------- src/chainrulescore.jl | 4 +- src/elbo.jl | 110 +++++++++--------- src/gf.jl | 59 +++++----- src/init_hybrid_params.jl | 1 + src/logden_normal.jl | 2 + test/test_HybridProblem.jl | 16 +-- test/test_doubleMM.jl | 28 ++--- test/test_elbo.jl | 94 +++++++-------- test/test_missingdriver.jl | 4 +- test/test_no_globals.jl | 4 +- 43 files changed, 368 insertions(+), 418 deletions(-) diff --git a/docs/src/tutorials/basic_cpu.md b/docs/src/tutorials/basic_cpu.md index ce6252c..18d8b38 100644 --- a/docs/src/tutorials/basic_cpu.md +++ b/docs/src/tutorials/basic_cpu.md @@ -5,12 +5,6 @@ CurrentModule = HybridVariationalInference ``` -Julias version: - -``` julia -VERSION -``` - First load necessary packages. ``` julia @@ -177,7 +171,7 @@ index to the sites inside a tuple. ``` julia n_batch = 20 train_dataloader = MLUtils.DataLoader( - values(train_data), batchsize=n_batch, partial=false) + CA.getdata.(values(train_data)), batchsize=n_batch, partial=false) ``` ## The Machine-Learning model @@ -244,7 +238,7 @@ invocation of the process based model (PBM), defined at the beginning. ``` julia approx = MeanHVIApproximation() f_batch = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1]) -ϕq0 = init_hybrid_ϕq(approx, θP, θM, transP; n_site) +ϕq0 = init_hybrid_ϕq(approx, θP, θM, transP; n_site, transM) prob = HybridProblem(θM, ϕq0, g_chain_scaled, ϕg0, f_batch, priors_dict, py, @@ -297,25 +291,25 @@ when running the model for all sites within one batch simultaneously. In the following, the PBM specification accepts matrices as arguments for parameters and drivers and returns a matrix of precitions. -For the parameters, one row corresponds to -one site. For the drivers and predictions, one column corresponds to one site. +Generally, the sites are the last dimension. So for the drivers and predictions, one column corresponds to one site. +However, for the parameters one row corresponds to one site. ``` julia using StaticArrays -function f_doubleMM_sites(θc::CA.ComponentMatrix, xPc::CA.ComponentMatrix) +function f_doubleMM_sites(θc_tr::CA.ComponentMatrix, xPc::CA.ComponentMatrix) # extract several covariates from xP S1 = view(xPc, Val(:S1), :) S2 = view(xPc, Val(:S2), :) # # extract the parameters as row-repeated vectors - # θc[:,:r0] is parameter r0 for each site in batch + # θc_tr[:,:r0] is parameter r0 for each site in batch # dot-multiplication of full matrix times row-vector repeats for each observation row # also introduces zero for missing observations, leading to zero gradient there is_valid = isfinite.(S1) .&& isfinite.(S2) - r0 = is_valid .* CA.getdata(θc[:, Val(:r0)])' - r1 = is_valid .* CA.getdata(θc[:, Val(:r1)])' - K1 = is_valid .* CA.getdata(θc[:, Val(:K1)])' - K2 = is_valid .* CA.getdata(θc[:, Val(:K2)])' + r0 = is_valid .* CA.getdata(θc_tr[:, Val(:r0)])' + r1 = is_valid .* CA.getdata(θc_tr[:, Val(:r1)])' + K1 = is_valid .* CA.getdata(θc_tr[:, Val(:K1)])' + K2 = is_valid .* CA.getdata(θc_tr[:, Val(:K2)])' # each variable is a matrix (n_obs x n_site) r0 .+ r1 .* S1 ./ (K1 .+ S1) .* S2 ./ (K2 .+ S2) end diff --git a/docs/src/tutorials/basic_cpu.qmd b/docs/src/tutorials/basic_cpu.qmd index 77621c1..86def5c 100644 --- a/docs/src/tutorials/basic_cpu.qmd +++ b/docs/src/tutorials/basic_cpu.qmd @@ -182,7 +182,7 @@ index to the sites inside a tuple. ```{julia} n_batch = 20 train_dataloader = MLUtils.DataLoader( - values(train_data), batchsize=n_batch, partial=false) + CA.getdata.(values(train_data)), batchsize=n_batch, partial=false) ``` ## The Machine-Learning model @@ -250,7 +250,7 @@ invocation of the process based model (PBM), defined at the beginning. ```{julia} approx = MeanHVIApproximation() f_batch = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1]) -ϕq0 = init_hybrid_ϕq(approx, θP, θM, transP; n_site) +ϕq0 = init_hybrid_ϕq(approx, θP, θM, transP; n_site, transM) prob = HybridProblem(θM, ϕq0, g_chain_scaled, ϕg0, f_batch, priors_dict, py, @@ -328,26 +328,26 @@ when running the model for all sites within one batch simultaneously. In the following, the PBM specification accepts matrices as arguments for parameters and drivers and returns a matrix of precitions. -For the parameters, one row corresponds to -one site. For the drivers and predictions, one column corresponds to one site. +Generally, the sites are the last dimension. So for the drivers and predictions, one column corresponds to one site. +However, for the parameters one row corresponds to one site. ```{julia} using StaticArrays -function f_doubleMM_sites(θc::CA.ComponentMatrix, xPc::CA.ComponentMatrix) +function f_doubleMM_sites(θc_tr::CA.ComponentMatrix, xPc::CA.ComponentMatrix) # extract several covariates from xP S1 = view(xPc, Val(:S1), :) S2 = view(xPc, Val(:S2), :) # # extract the parameters as row-repeated vectors - # θc[:,:r0] is parameter r0 for each site in batch + # θc_tr[:,:r0] is parameter r0 for each site in batch # dot-multiplication of full matrix times row-vector repeats for each observation row # also introduces zero for missing observations, leading to zero gradient there is_valid = isfinite.(S1) .&& isfinite.(S2) - r0 = is_valid .* CA.getdata(θc[:, Val(:r0)])' - r1 = is_valid .* CA.getdata(θc[:, Val(:r1)])' - K1 = is_valid .* CA.getdata(θc[:, Val(:K1)])' - K2 = is_valid .* CA.getdata(θc[:, Val(:K2)])' + r0 = is_valid .* CA.getdata(θc_tr[:, Val(:r0)])' + r1 = is_valid .* CA.getdata(θc_tr[:, Val(:r1)])' + K1 = is_valid .* CA.getdata(θc_tr[:, Val(:K1)])' + K2 = is_valid .* CA.getdata(θc_tr[:, Val(:K2)])' # each variable is a matrix (n_obs x n_site) r0 .+ r1 .* S1 ./ (K1 .+ S1) .* S2 ./ (K2 .+ S2) end diff --git a/docs/src/tutorials/blocks_corr.md b/docs/src/tutorials/blocks_corr.md index 5244069..75459aa 100644 --- a/docs/src/tutorials/blocks_corr.md +++ b/docs/src/tutorials/blocks_corr.md @@ -106,15 +106,15 @@ First, draw a sample. ``` julia n_sample_pred = 400 -(y_cor, θsP_cor, θsMs_cor) = (; y, θsP, θsMs) = predict_hvi( +(y_cor, θsP_cor, θsMs_tr_cor) = (; y, θsP, θsMs_tr) = predict_hvi( Random.default_rng(), probo_cor; n_sample_pred) -(y_ind, θsP_ind, θsMs_ind) = (; y, θsP, θsMs) = predict_hvi( +(y_ind, θsP_ind, θsMs_tr_ind) = (; y, θsP, θsMs_tr) = predict_hvi( Random.default_rng(), probo_ind; n_sample_pred) ``` ``` julia i_site = 1 -θ1 = vcat(θsP_ind, θsMs_ind[i_site,:,:]) +θ1 = vcat(θsP_ind, θsMs_tr_ind[i_site,:,:]) θ1_nt = NamedTuple(k => CA.getdata(θ1[k,:]) for k in keys(θ1[:,1])) # plt = pairplot(θ1_nt) ``` @@ -142,11 +142,11 @@ fig ``` julia plot_sd_vs_mean = (par) -> begin fig = Figure(); ax = Axis(fig[1,1], xlabel="mean($par)",ylabel="sd($par)") - θmean_cor = [mean(θsMs_cor[s,par,:]) for s in axes(θsMs_cor, 1)] - θsd_cor = [std(θsMs_cor[s,par,:]) for s in axes(θsMs_cor, 1)] + θmean_cor = [mean(θsMs_tr_cor[s,par,:]) for s in axes(θsMs_tr_cor, 1)] + θsd_cor = [std(θsMs_tr_cor[s,par,:]) for s in axes(θsMs_tr_cor, 1)] scatter!(ax, θmean_cor, θsd_cor, label="correlated") - θmean_ind = [mean(θsMs_ind[s,par,:]) for s in axes(θsMs_ind, 1)] - θsd_ind = [std(θsMs_ind[s,par,:]) for s in axes(θsMs_ind, 1)] + θmean_ind = [mean(θsMs_tr_ind[s,par,:]) for s in axes(θsMs_tr_ind, 1)] + θsd_ind = [std(θsMs_tr_ind[s,par,:]) for s in axes(θsMs_tr_ind, 1)] scatter!(ax, θmean_ind, θsd_ind, label="independent") axislegend(ax, unique=true) fig diff --git a/docs/src/tutorials/blocks_corr.qmd b/docs/src/tutorials/blocks_corr.qmd index 9a09bb0..e5f2c71 100644 --- a/docs/src/tutorials/blocks_corr.qmd +++ b/docs/src/tutorials/blocks_corr.qmd @@ -109,16 +109,16 @@ solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) First, draw a sample. ```{julia} n_sample_pred = 400 -(y_cor, θsP_cor, θsMs_cor) = (; y, θsP, θsMs) = predict_hvi( +(y_cor, θsP_cor, θsMs_tr_cor) = (; y, θsP, θsMs_tr) = predict_hvi( Random.default_rng(), probo_cor; n_sample_pred) -(y_ind, θsP_ind, θsMs_ind) = (; y, θsP, θsMs) = predict_hvi( +(y_ind, θsP_ind, θsMs_tr_ind) = (; y, θsP, θsMs_tr) = predict_hvi( Random.default_rng(), probo_ind; n_sample_pred) ``` ```{julia} #| output: true i_site = 1 -θ1 = vcat(θsP_ind, θsMs_ind[i_site,:,:]) +θ1 = vcat(θsP_ind, θsMs_tr_ind[i_site,:,:]) θ1_nt = NamedTuple(k => CA.getdata(θ1[k,:]) for k in keys(θ1[:,1])) # plt = pairplot(θ1_nt) ``` @@ -143,11 +143,11 @@ fig #| output: true plot_sd_vs_mean = (par) -> begin fig = Figure(); ax = Axis(fig[1,1], xlabel="mean($par)",ylabel="sd($par)") - θmean_cor = [mean(θsMs_cor[s,par,:]) for s in axes(θsMs_cor, 1)] - θsd_cor = [std(θsMs_cor[s,par,:]) for s in axes(θsMs_cor, 1)] + θmean_cor = [mean(θsMs_tr_cor[s,par,:]) for s in axes(θsMs_tr_cor, 1)] + θsd_cor = [std(θsMs_tr_cor[s,par,:]) for s in axes(θsMs_tr_cor, 1)] scatter!(ax, θmean_cor, θsd_cor, label="correlated") - θmean_ind = [mean(θsMs_ind[s,par,:]) for s in axes(θsMs_ind, 1)] - θsd_ind = [std(θsMs_ind[s,par,:]) for s in axes(θsMs_ind, 1)] + θmean_ind = [mean(θsMs_tr_ind[s,par,:]) for s in axes(θsMs_tr_ind, 1)] + θsd_ind = [std(θsMs_tr_ind[s,par,:]) for s in axes(θsMs_tr_ind, 1)] scatter!(ax, θmean_ind, θsd_ind, label="independent") axislegend(ax, unique=true) fig diff --git a/docs/src/tutorials/blocks_corr_files/figure-commonmark/cell-10-output-1.png b/docs/src/tutorials/blocks_corr_files/figure-commonmark/cell-10-output-1.png index 6b042a815a402cf3a59ead3e1917b536aee30009..d620c2b46b265dd0de3784a88907452ad5c4e990 100644 GIT binary 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