implement ML model depending on thetaP

* allow constraining mean of PBL parameters to initial values
* inspect elbo components of theta-contrained vs unconstrained HVI fits
* move CUDA dependency to extension and remove GPUDataHandler, replace by MLDataDevices
* factor predict_ζf out of predict_gf
** for inspecting NUTS generated samples on unconstrained scale
* fix error in  confusing parameter positions
** depending on scenario-templates need to construct different Interpreters passed to forward model
* compare plots of marginal posterior densities  of several sites
* compare HVI to HMC inversion
* implement ML model depending on thetaP
* default predict_gf for all sites of dataloader of problem
* return updated problem from solvers

Author: bgctw

.github/workflows/CompatHelper.yml

@@ -14,13 +14,13 @@ jobs:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
         run: julia -e 'using CompatHelper; CompatHelper.main()'
-  keepalive-job:
-    name: Keepalive Workflow
-    runs-on: ubuntu-latest
-    permissions:
-      actions: write
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: 'keepalive'      # The branch, tag or SHA to checkout.       
-      - uses: gautamkrishnar/keepalive-workflow@v2
+  # keepalive-job:
+  #   name: Keepalive Workflow
+  #   runs-on: ubuntu-latest
+  #   permissions:
+  #     actions: write
+  #   steps:
+  #     - uses: actions/checkout@v4
+  #       with:
+  #         ref: 'keepalive'      # The branch, tag or SHA to checkout.       
+  #     - uses: gautamkrishnar/keepalive-workflow@v2

.gitignore

@@ -7,3 +7,6 @@
 test/Manifest*.toml
 dev/Manifest*.toml
 tmp/
+**/tmp.svg
+dev/intermediate/*
+dev/tmp.pdf

Project.toml

@@ -6,55 +6,60 @@ version = "1.0.0-DEV"
 [deps]
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
 BlockDiagonals = "0a1fb500-61f7-11e9-3c65-f5ef3456f9f0"
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 DistributionFits = "45214091-1ed4-4409-9bcf-fdb48a05e921"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLDataDevices = "7e8f7934-dd98-4c1a-8fe8-92b47a384d40"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [weakdeps]
+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"
 HybridVariationalInferenceFluxExt = "Flux"
 HybridVariationalInferenceLuxExt = "Lux"
 HybridVariationalInferenceSimpleChainsExt = "SimpleChains"
 
 [compat]
-Bijectors = "0.15.4"
-BlockDiagonals = "0.1.42"
-CUDA = "5.5.2"
+Bijectors = "0.14, 0.15"
+BlockDiagonals = "0.1.42, 0.2"
+CUDA = "5.7"
 ChainRulesCore = "1.25"
 Combinatorics = "1.0.2"
 CommonSolve = "0.2.4"
 ComponentArrays = "0.15.19"
 DistributionFits = "0.3.9"
 Distributions = "0.25.117"
-Flux = "v0.15.2, 0.16"
+Flux = "0.14, 0.15, 0.16"
+Functors = "0.4, 0.5"
 GPUArraysCore = "0.1, 0.2"
-LinearAlgebra = "1.10.0"
+LinearAlgebra = "1.10"
 Lux = "1.4.2"
-MLDataDevices = "1.6.9"
+MLDataDevices = "1.5, 1.6"
 MLUtils = "0.4.5"
 Optimization = "3.19.3, 4"
 Random = "1.10.0"
 SimpleChains = "0.4"
+StableRNGs = "1.0.2"
+StaticArrays = "1.9.13"
 StatsBase = "0.34.4"
 StatsFuns = "1.3.2"
-Zygote = "0.6.73, 0.7"
 julia = "1.10"
 
 [workspace]

_typos.toml

@@ -1,2 +1,3 @@
 [default.extend-words]
-SOM = "SOM"
+SOM = "SOM"
+negLogLik = "negLogLik"

dev/Project.toml

@@ -4,18 +4,24 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DistributionFits = "45214091-1ed4-4409-9bcf-fdb48a05e921"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 HybridVariationalInference = "a108c475-a4e2-4021-9a84-cfa7df242f64"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
+MLDataDevices = "7e8f7934-dd98-4c1a-8fe8-92b47a384d40"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
+PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SimpleChains = "de6bee2f-e2f4-4ec7-b6ed-219cc6f6e9e5"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
+Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"