diff --git a/experiments/Synth/.envrc b/experiments/Synth/.envrc deleted file mode 100644 index 3550a30..0000000 --- a/experiments/Synth/.envrc +++ /dev/null @@ -1 +0,0 @@ -use flake diff --git a/experiments/Synth/.gitattributes b/experiments/Synth/.gitattributes deleted file mode 100644 index eb70581..0000000 --- a/experiments/Synth/.gitattributes +++ /dev/null @@ -1,219 +0,0 @@ -# This global .gitattributes file is adhered to via command: -# git config --global core.attributesfile ~/git-global/.gitattributes_global -# -# Use this file on every device where git development is done. - - -### -### Auto detect text files and perform LF normalization -### -# http://davidlaing.com/2012/09/19/customise-your-gitattributes-to-become-a-git-ninja/ -* text=auto - -### -### The above will handle all files NOT found below. -### From https://github.com/alexkaratarakis/gitattributes -### - -### gitattributes/C++.gitattributes d3b93d4 on Jun 20, 2018 -#sources -*.c text diff=cpp -*.cc text diff=cpp -*.cxx text diff=cpp -*.cpp text diff=cpp -*.c++ text diff=cpp -*.hpp text diff=cpp -*.h text diff=cpp -*.h++ text diff=cpp -*.hh text diff=cpp - -# Compiled Object files -*.slo binary -*.lo binary -*.o binary -*.obj binary - -# Precompiled Headers -*.gch binary -*.pch binary - -# Compiled Dynamic libraries -*.so binary -*.dylib binary -*.dll binary - -# Compiled Static libraries -*.lai binary -*.la binary -*.a binary -*.lib binary - -# Executables -*.exe binary -*.out binary -*.app binary - -# Other -############## -*.exe binary -*.num binary -*.xls binary -*.xlsx binary -*.XLS binary -*.XLSX binary -bin/ binary - -### gitattributes/Common.gitattributes 9b38185 on Jun 9, 2018 -# Common settings that generally should always be used with your language specific settings - -# Auto detect text files and perform LF normalization -# http://davidlaing.com/2012/09/19/customise-your-gitattributes-to-become-a-git-ninja/ -* text=auto - -# -# The above will handle all files NOT found below -# - -# Documents -*.doc diff=astextplain -*.DOC diff=astextplain -*.docx diff=astextplain -*.DOCX diff=astextplain -*.dot diff=astextplain -*.DOT diff=astextplain -*.pdf diff=astextplain -*.PDF diff=astextplain -*.rtf diff=astextplain -*.RTF diff=astextplain -*.md text diff=markdown -*.adoc text -*.textile text -*.tex text diff=tex -*.mustache text -*.csv text -*.tab text -*.tsv text -*.sql text - -# Graphics -*.png binary -*.jpg binary -*.jpeg binary -*.gif binary -*.tif binary -*.tiff binary -*.ico binary -# SVG treated as an asset (binary) by default. 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-# Source files -# ============ -*.m text -*.mu text - -# Caution: *.m also matches Mathematica packages. - -# Binary files -# ============ -*.p binary -*.mex* binary -*.fig binary -*.mat binary -*.mdl binary -*.slx binary -*.mdlp binary -*.slxp binary -*.sldd binary -*.mltbx binary -*.mlappinstall binary -*.mlpkginstall binary -*.mn binary - -### gitattributes/Python.gitattributes cbd3af4 on Jun 20, 2018 -# Basic .gitattributes for a python repo. - -# Source files -# ============ -*.pxd text diff=python -*.py text diff=python -*.py3 text diff=python -*.pyw text diff=python -*.pyx text diff=python - -# Binary files -# ============ -*.db binary -*.p binary -*.pkl binary -*.pyc binary -*.pyd binary -*.pyo binary - -# Note: .db, .p, and .pkl files are associated -# with the python modules ``pickle``, ``dbm.*``, -# ``shelve``, ``marshal``, ``anydbm``, & ``bsddb`` -# (among others). - -### gitattributes/R.gitattributes 80aacc1 on Aug 22, 2015 -# Basic .gitattributes for a R repo. - -# Source files -# ============ -*.Rdata text -*.rdb binary -*.rds binary -*.Rd text -*.Rdx binary -*.Rmd text -*.R text diff --git a/experiments/Synth/.github/workflows/CI.yml b/experiments/Synth/.github/workflows/CI.yml deleted file mode 100644 index 4d8415c..0000000 --- a/experiments/Synth/.github/workflows/CI.yml +++ /dev/null @@ -1,37 +0,0 @@ -name: CI -on: - push: - branches: - - main - tags: "*" - pull_request: -concurrency: - # Skip intermediate builds: always. - # Cancel intermediate builds: only if it is a pull request build. - group: ${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }} -jobs: - test: - name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }} - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - version: - - "1" - os: - - ubuntu-latest - arch: - - x64 - steps: - - uses: actions/checkout@v4 - - uses: julia-actions/setup-julia@v1 - with: - version: ${{ matrix.version }} - arch: ${{ matrix.arch }} - - uses: julia-actions/cache@v1 - - uses: julia-actions/julia-buildpkg@v1 - - - name: Run tests - run: > - julia --project=. --color=yes test/runtests.jl diff --git a/experiments/Synth/.gitignore b/experiments/Synth/.gitignore deleted file mode 100644 index f9dfacf..0000000 --- a/experiments/Synth/.gitignore +++ /dev/null @@ -1,417 +0,0 @@ -################################################################################ -# DrWatson Project Structure # -################################################################################ - 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-[[deps.gperf_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "0ba42241cb6809f1a278d0bcb976e0483c3f1f2d" -uuid = "1a1c6b14-54f6-533d-8383-74cd7377aa70" -version = "3.1.1+1" - -[[deps.libaom_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "522c1df09d05a71785765d19c9524661234738e9" -uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" -version = "3.11.0+0" - -[[deps.libass_jll]] -deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Zlib_jll"] -git-tree-sha1 = "e17c115d55c5fbb7e52ebedb427a0dca79d4484e" -uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" -version = "0.15.2+0" - -[[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.8.0+1" - -[[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", "Pkg"] -git-tree-sha1 = "141fe65dc3efabb0b1d5ba74e91f6ad26f84cc22" -uuid = "2db6ffa8-e38f-5e21-84af-90c45d0032cc" -version = "1.11.0+0" - -[[deps.libfdk_aac_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "8a22cf860a7d27e4f3498a0fe0811a7957badb38" -uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" -version = "2.0.3+0" - -[[deps.libinput_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "eudev_jll", "libevdev_jll", "mtdev_jll"] -git-tree-sha1 = "ad50e5b90f222cfe78aa3d5183a20a12de1322ce" -uuid = "36db933b-70db-51c0-b978-0f229ee0e533" -version = "1.18.0+0" - -[[deps.libpng_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"] -git-tree-sha1 = "055a96774f383318750a1a5e10fd4151f04c29c5" -uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" -version = "1.6.46+0" - -[[deps.libvorbis_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] -git-tree-sha1 = "490376214c4721cdaca654041f635213c6165cb3" -uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" -version = "1.3.7+2" - -[[deps.micromamba_jll]] -deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl"] -git-tree-sha1 = "b4a5a3943078f9fd11ae0b5ab1bdbf7718617945" -uuid = "f8abcde7-e9b7-5caa-b8af-a437887ae8e4" -version = "1.5.8+0" - -[[deps.mtdev_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "814e154bdb7be91d78b6802843f76b6ece642f11" -uuid = "009596ad-96f7-51b1-9f1b-5ce2d5e8a71e" -version = "1.1.6+0" - -[[deps.nghttp2_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.52.0+1" - -[[deps.oneTBB_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "d5a767a3bb77135a99e433afe0eb14cd7f6914c3" -uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e" -version = "2022.0.0+0" - -[[deps.p7zip_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -version = "17.4.0+2" - -[[deps.pixi_jll]] -deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl"] -git-tree-sha1 = "f349584316617063160a947a82638f7611a8ef0f" -uuid = "4d7b5844-a134-5dcd-ac86-c8f19cd51bed" -version = "0.41.3+0" - -[[deps.x264_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" -uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" -version = "2021.5.5+0" - -[[deps.x265_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" -uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" -version = "3.5.0+0" - -[[deps.xkbcommon_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] -git-tree-sha1 = "63406453ed9b33a0df95d570816d5366c92b7809" -uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "1.4.1+2" diff --git a/experiments/Synth/Project.toml b/experiments/Synth/Project.toml deleted file mode 100644 index 384d500..0000000 --- a/experiments/Synth/Project.toml +++ /dev/null @@ -1,50 +0,0 @@ -name = "Synth" - -[deps] -AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" -Attractors = "f3fd9213-ca85-4dba-9dfd-7fc91308fec7" -BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" -CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -Clingo_jll = "e1d4ad0e-dc8b-5d2d-8155-e9a55b531f08" -CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" -DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" -DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1" -DynamicalSystems = "61744808-ddfa-5f27-97ff-6e42cc95d634" -Git = "d7ba0133-e1db-5d97-8f8c-041e4b3a1eb2" -GraphDynamicalSystems = "13529e2e-ed53-56b1-bd6f-420b01fca819" -GraphRecipes = "bd48cda9-67a9-57be-86fa-5b3c104eda73" -Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" -Herb = "c09c6b7f-4f63-49de-90d9-97a3563c0f4a" -HerbConstraints = "1fa96474-3206-4513-b4fa-23913f296dfc" -HerbCore = "2b23ba43-8213-43cb-b5ea-38c12b45bd45" -HerbGrammar = "4ef9e186-2fe5-4b24-8de7-9f7291f24af7" -HerbSearch = "3008d8e8-f9aa-438a-92ed-26e9c7b4829f" -HerbSpecification = "6d54aada-062f-46d8-85cf-a1ceaf058a06" -JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" -JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078" -MetaGraphsNext = "fa8bd995-216d-47f1-8a91-f3b68fbeb377" -ParserCombinator = "fae87a5f-d1ad-5cf0-8f61-c941e1580b46" -PlotlyJS = "f0f68f2c-4968-5e81-91da-67840de0976a" -Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781" -PlutoTeachingTools = "661c6b06-c737-4d37-b85c-46df65de6f69" -PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" -PreferenceTools = "ba661fbb-e901-4445-b070-854aec6bfbc5" -ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568" -ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca" -PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" -Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" -SlurmClusterManager = "c82cd089-7bf7-41d7-976b-6b5d413cbe0a" -SoleLogics = "b002da8f-3cb3-4d91-bbe3-2953433912b5" -StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" -Term = "22787eb5-b846-44ae-b979-8e399b8463ab" -TidierData = "fe2206b3-d496-4ee9-a338-6a095c4ece80" -UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228" -XML = "72c71f33-b9b6-44de-8c94-c961784809e2" - -[compat] -DrWatson = "2.16.0" -julia = "1.10.2" diff --git a/experiments/Synth/README.md b/experiments/Synth/README.md deleted file mode 100644 index 3fa85c9..0000000 --- a/experiments/Synth/README.md +++ /dev/null @@ -1,31 +0,0 @@ -# Synth - -This code base is using the [Julia Language](https://julialang.org/) and -[DrWatson](https://juliadynamics.github.io/DrWatson.jl/stable/) -to make a reproducible scientific project named -> Synth - -To (locally) reproduce this project, do the following: - -0. Download this code base. Notice that raw data are typically not included in the - git-history and may need to be downloaded independently. -1. Open a Julia console and do: - - ```julia - julia> using Pkg - julia> Pkg.add("DrWatson") # install globally, for using `quickactivate` - julia> Pkg.activate("path/to/this/project") - julia> Pkg.instantiate() - ``` - -This will install all necessary packages for you to be able to run the scripts and -everything should work out of the box, including correctly finding local paths. - -You may notice that most scripts start with the commands: - -```julia -using DrWatson -@quickactivate "Synth" -``` - -which auto-activate the project and enable local path handling from DrWatson. diff --git a/experiments/Synth/flake.lock b/experiments/Synth/flake.lock deleted file mode 100644 index e841dc9..0000000 --- a/experiments/Synth/flake.lock +++ /dev/null @@ -1,73 +0,0 @@ -{ - "nodes": { - "nixpkgs": { - "locked": { - "lastModified": 0, - "narHash": "sha256-JwQZIGSYnRNOgDDoIgqKITrPVil+RMWHsZH1eE1VGN0=", - "path": "/nix/store/gg86rfp39vc7chqsszk32q7995hz4943-source", - "type": "path" - }, - "original": { - "id": "nixpkgs", - "type": "indirect" - } - }, - "old-julia": { - "locked": { - "lastModified": 1714750952, - "narHash": "sha256-oOUdvPrO8CbupgDSaPou+Jv6GL+uQA2QlE33D7OLzkM=", - "owner": "NixOS", - "repo": "nixpkgs", - "rev": "5fd8536a9a5932d4ae8de52b7dc08d92041237fc", - "type": "github" - }, - "original": { - "id": "nixpkgs", - "rev": "5fd8536a9a5932d4ae8de52b7dc08d92041237fc", - "type": "indirect" - } - }, - "root": { - "inputs": { - "nixpkgs": "nixpkgs", - "old-julia": "old-julia", - "utils": "utils" - } - }, - "systems": { - "locked": { - "lastModified": 1681028828, - "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", - "owner": "nix-systems", - "repo": "default", - "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", - "type": "github" - }, - "original": { - "owner": "nix-systems", - "repo": "default", - "type": "github" - } - }, - "utils": { - "inputs": { - "systems": "systems" - }, - "locked": { - "lastModified": 1731533236, - "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=", - "owner": "numtide", - "repo": "flake-utils", - "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b", - "type": "github" - }, - "original": { - "owner": "numtide", - "repo": "flake-utils", - "type": "github" - } - } - }, - "root": "root", - "version": 7 -} diff --git a/experiments/Synth/flake.nix b/experiments/Synth/flake.nix deleted file mode 100644 index 0570c59..0000000 --- a/experiments/Synth/flake.nix +++ /dev/null @@ -1,19 +0,0 @@ -{ - inputs = { - utils.url = "github:numtide/flake-utils"; - old-julia.url = "nixpkgs/5fd8536a9a5932d4ae8de52b7dc08d92041237fc"; - }; - outputs = { self, nixpkgs, utils, old-julia }: utils.lib.eachDefaultSystem (system: - let - pkgs = nixpkgs.legacyPackages.${system}; - julia1-10-2-pkgs = old-julia.legacyPackages.${system}; - in - { - devShell = pkgs.mkShell { - buildInputs = [ - julia1-10-2-pkgs.julia-bin - ]; - }; - } - ); -} diff --git a/experiments/Synth/notebooks/biodivine.jl b/experiments/Synth/notebooks/biodivine.jl deleted file mode 100644 index bf8a9cf..0000000 --- a/experiments/Synth/notebooks/biodivine.jl +++ /dev/null @@ -1,420 +0,0 @@ -### A Pluto.jl notebook ### -# v0.19.46 - -using Markdown -using InteractiveUtils - -# ╔═╡ 1a97f1a6-8a0d-11ef-3a8e-8da68a63985b -using DrWatson - -# ╔═╡ 6d4d6baa-d160-47f3-bf7c-a9e0fabdf3c2 -@quickactivate - -# ╔═╡ 0a6d4236-f1ac-474d-be98-3c2a53dd9a46 -using Graphs - -# ╔═╡ 95417510-36db-451e-ba87-9faa23f7d412 -using SoleLogics: Atom, subformulas, Formula - -# ╔═╡ 4a7c619f-6ad6-43d6-b8e8-bddf4255bf17 -using MetaGraphsNext: MetaGraph - -# ╔═╡ 4aced393-1873-4fbf-b582-65a20346c64a -using Plots - -# ╔═╡ 3ab50cf5-da53-440c-9796-c9ee73b7ad86 -using Synth: AEONParser - -# ╔═╡ 8a5e88c4-b0a6-4848-b6d2-74bdbd960e7c -using DataFrames - -# ╔═╡ 8aaf540d-4e8b-499c-818f-3ce9f53a03cf -using JLD2 - -# ╔═╡ 6ef6f82a-5869-42c6-9111-ae125414ab1c -using StatsPlots - -# ╔═╡ 0f49994b-f5fe-4083-b06e-2a4b99ed9200 -using TidierData - -# ╔═╡ 1dd2589a-baad-415a-a842-9f3701812f8e -using PlutoUI - -# ╔═╡ d202a3b0-913d-4a63-a81c-cfb64f2e5176 -using PlutoTeachingTools - -# ╔═╡ 7cb5d22d-4a2e-483e-a567-77c09fda306b -using AbstractTrees - -# ╔═╡ 016ede63-6f1c-4d38-81bf-77b4538c4705 -md""" -## Variables -""" - -# ╔═╡ 1121b33a-d9e9-4cb8-b15e-2a75d98453d7 -md""" -## Inputs -""" - -# ╔═╡ 253c2d2c-5d77-4de6-bb00-27eb4b0ebc07 -md""" -## Regulations/Edges -""" - -# ╔═╡ dc0fcf4b-ccb4-4e28-8ffb-268145008f23 -md""" -Average number of reglations/edges per node. Note that this is not necessarily proportional to the size of the target function. -""" - -# ╔═╡ 6f40fcb3-d8d1-4ade-a9ea-12fc58975f60 -md""" -# Experiments -## \# Trajectories vs. Models Found - -When varying the number of trajectories used for synthesis, we expect to see the number of models found to decrease, because more trajectories/information makes the specification more specific, limiting the size of the space of valid models. - -We might expect to see some exponential decay like - -```math -y = e^{-x/a} * b -``` - -with some rate `a` and scaling `b`. -""" - -# ╔═╡ cc79a481-8bc9-442d-a129-a7f804ba0ccd -y₁ = [(x, ℯ^(-x / 200) * 100_000) for x = 0:25:1000]; - -# ╔═╡ 6989f31e-2256-4532-b885-e5354ed9e99a -scatter( - y₁, - xlabel = "# of Trajectories", - ylabel = "# of models found per node", - labels = "", - xlims = [0, 1000], - ylims = [0, 100000], -) - -# ╔═╡ 39e255ea-1251-4b3b-a5bc-0cd65491020b -md""" -Notes -- Fix number of iterations, size of target functions -- Plot per node -- Size of the network as weighting for the points on the scatterplot -- Filter such that none of the trajectories have the same initial condition -""" - -# ╔═╡ 1f8e149a-0c73-48b1-b9f1-74887c5fdeac -md""" -## \# Iterations until First Model - -- Per node -- Weighting based on network size -""" - -# ╔═╡ 99efeb26-41a8-4600-9aa8-8c9b88987ad7 -histogram() - -# ╔═╡ 196936b8-2dbf-4c56-8544-4a66a8b911af -md""" -## Average Deg. of Networks -""" - -# ╔═╡ 847d131e-f310-4d43-80b4-c120c9832eee -md""" -## Network Size vs. \# Models Found - -For a fixed number of trajectories. -""" - -# ╔═╡ 0bac6f39-6683-4a08-bdad-31d232be36e7 -plot() - -# ╔═╡ b6b12ecb-995d-4fd2-82cf-0485c4ff1cf1 -md""" -Notes -- Average per node -- Limit function sizes - - Implicitely limits iterations -""" - -# ╔═╡ 84fda41b-1309-4a58-be79-b517baf0814f -md""" -## Global Clustering Coef. vs. \# Models Found - -- [Global clustering coefficient on Wikipedia](https://en.wikipedia.org/wiki/Clustering_coefficient#Global_clustering_coefficient) -""" - -# ╔═╡ e5636bd9-5e8e-46dd-9b6c-a66497d7df95 -plot() - -# ╔═╡ c4a84267-fc57-4a5d-b2ae-5db24896108d -md""" -# Appendix - -📊 Data loading, packages, etc. etc. - -## Data, Theme - -Loading files, setting up colors, etc. -""" - -# ╔═╡ 74c47afc-98ab-44a3-bc8b-2a104fbffb07 -plot_color_theme = cgrad(:matter, 5, categorical = true) - -# ╔═╡ a2e21a23-3fc0-492a-9b27-cb7f307fcb1d -excluded_files = [r"041\.aeon\.jld2", r"079\.aeon\.jld2"] - -# ╔═╡ decf04d3-3ce0-4d3d-a046-1989994ad874 -begin - df = collect_results!( - datadir("src_parsed", "biodivine_benchmark"); - rexclude = excluded_files, - ) - # "full/path/to/001.aeon.jld2" -> "001" - df.ID = map((x -> x[1]) ∘ splitext ∘ (x -> x[1]) ∘ splitext ∘ basename, df.path) -end - -# ╔═╡ f1a1afae-a944-4cc8-8684-de76e8eb7922 -md""" -# BN Synthesis with Biodivine Benchmarks - -🧑‍💻 [github.com/sybila/biodivine-boolean-models](https://github.com/sybila/biodivine-boolean-models) - -$((!isdefined(@__MODULE__, :df) || nrow(df) == 0) ? danger(md"It seems that the dataframe with the benchmark information failed to load, or it was empty. Try running the script: - -`julia experiments/Synth/scripts/biodivine_benchmark/load_aeon.jl` - -to load/parse the benchmark.") : md"") -""" - -# ╔═╡ 94df90c5-8431-4fca-a611-4a0ebcd14e80 -md""" -## Benchmark Overview - -The Biodivine Boolean model benchmark includes -$(length(excluded_files) > 0 ? nrow(df) + length(excluded_files) : nrow(df)) -models. -$(length(excluded_files) > 0 ? "We currently drop two (#079, and #041) because 79 causes JLD2 to crash upon deserializing, and 41 causes some weird `#undef` error." : "") - -The models have a varying number of `variables`, `inputs` (nodes with no incoming `regulations`/`edges`), and `regulations` (`edges`). -""" - -# ╔═╡ ca807ea7-1980-4800-91f8-7b446923127a -components_df = let UpdateFunction = AEONParser.UpdateFunction - @chain df begin - @select parsed_model ID - flatten(:parsed_model) - # Twice because I've accidentally added each component as a vector of length 1 - flatten(:parsed_model) - @rename Component = parsed_model # new = old - @mutate ComponentType = typeof(Component) - @group_by ComponentType - end -end; - -# ╔═╡ ed381482-fc40-4378-90b0-df86a474c864 -just_update_functions = components_df[(ComponentType = AEONParser.UpdateFunction,)]; - -# ╔═╡ a0f79b65-31d0-4faa-b87a-28335501ed61 -update_functions = @chain just_update_functions begin - @group_by ID - @select Component -end; - -# ╔═╡ 022aad38-22e5-4d4d-a29a-3c4b900ab974 -function update_functions_to_network( - update_functions::AbstractVector{<:AEONParser.UpdateFunction}, -) - network = MetaGraph(SimpleDiGraph(); label_type = String, vertex_data_type = Formula) - - for up in update_functions - network[up.target.name] = up.fn - end - - for up in update_functions - atoms = filter(x -> isa(x, Atom), subformulas(up.fn)) - for atom in atoms - source = atom.value - add_edge!(network, up.target.name, source) - end - end - - return network -end - -# ╔═╡ baff69b5-5af7-4aaf-a1eb-846216deffc5 -metagraph_models = combine( - update_functions[1:20], - :Component => - x -> - update_functions_to_network(Vector{AEONParser.UpdateFunction}(x)) => - :Network, -); - -# ╔═╡ e7ad15ac-8976-4100-83ce-e964a6489605 -Dict(names(metagraph_models[1, :]) .=> values(metagraph_models[1, :])) - -# ╔═╡ 4d26800f-12b6-4ffe-b6e1-8db8834a8da9 -with_fn_arity_df = let UpdateFunction = AEONParser.UpdateFunction - with_fn_arity_df = deepcopy(just_update_functions) - with_fn_arity_df[!, :Arity] .= - length.(collect.(Leaves.(map(x -> x.fn, just_update_functions[!, :Component])))) - with_fn_arity_df -end; - -# ╔═╡ c30f1640-42f4-4538-a5e0-ede64784bed1 -with_mean_arity_df = @chain with_fn_arity_df begin - @group_by ID - @summarize mean_arity = mean(Arity) -end; - -# ╔═╡ 7f4d4333-d66e-4ddb-8448-21db9bdc6c6d -histogram( - with_mean_arity_df.mean_arity, - ylim = (0, 120), - color = cgrad(:matter, 5, categorical = true)[1], - legend = nothing, - xlabel = "Mean Update Function Arity", - ylabel = "# Models", -) - -# ╔═╡ e4f087f0-4c83-408d-a9a1-5bb1447a1da7 -raw_summary_df = load(datadir("src_parsed", "summary_biodivine_benchmark.jld2"))["df"]; - -# ╔═╡ fab25481-21f9-490f-974f-6ea88177f68f -summary_names = map(strip, names(raw_summary_df)) - -# ╔═╡ 9f2c259a-479d-483a-9cf5-c249b6049dd6 -summary_df = rename(raw_summary_df, summary_names); - -# ╔═╡ 30597336-77c9-4d68-a9b4-065469b1b228 -histogram( - summary_df.variables, - ylims = (0, 100), - xlabel = "Variables", - ylabel = "# Models", - legend = nothing, - color = plot_color_theme[1], -) - -# ╔═╡ 2bcbfef7-c44b-413e-929e-d6535ef0edf3 -histogram( - summary_df.inputs, - ylim = (0, 120), - xlabel = "Inputs", - ylabel = "# Models", - legend = nothing, - color = cgrad(:matter, 5, categorical = true)[2], -) - -# ╔═╡ 1da7e7ee-99a2-4e3e-9ffc-ee2b246f5da4 -histogram( - summary_df.regulations, - ylim = (0, 80), - color = cgrad(:matter, 5, categorical = true)[3], - legend = nothing, - xlabel = "Regulations", - ylabel = "# Models", -) - -# ╔═╡ ff760751-c7e6-4085-b96e-45e2732f6b5f -avg_deg = histogram( - summary_df.regulations ./ summary_df.variables, - ylim = (0, 80), - color = cgrad(:matter, 5, categorical = true)[4], - legend = nothing, - xlabel = "Regulations / Node", - ylabel = "# Models", -) - - -# ╔═╡ 658bb88d-274f-4807-b18d-966eb9424e28 -avg_deg - -# ╔═╡ 28a1addd-059c-419c-ba7b-1564b875f0e6 -summary_stats = describe(summary_df); - -# ╔═╡ 813cd252-659f-4ee5-89ad-d78a183928f3 -TableOfContents() - -# ╔═╡ 76ec0b17-05ad-40bd-b922-6ad14e619013 -space = html"


" - -# ╔═╡ 21d9f3cd-d68d-4f9d-9b3c-d31d7383c872 -space - -# ╔═╡ 55e55e42-9186-4e04-a7f4-f433499b62b0 -space - -# ╔═╡ 9481ea51-b479-42a4-a5e8-03e0c4ea7387 -md""" -## Packages Used - -Keeping things tidy 🧹 -""" - -# ╔═╡ 31dff5a3-6912-42d1-a8dd-932bb618e21f -plotly() - -# ╔═╡ Cell order: -# ╟─f1a1afae-a944-4cc8-8684-de76e8eb7922 -# ╟─94df90c5-8431-4fca-a611-4a0ebcd14e80 -# ╟─016ede63-6f1c-4d38-81bf-77b4538c4705 -# ╟─30597336-77c9-4d68-a9b4-065469b1b228 -# ╟─1121b33a-d9e9-4cb8-b15e-2a75d98453d7 -# ╟─2bcbfef7-c44b-413e-929e-d6535ef0edf3 -# ╟─253c2d2c-5d77-4de6-bb00-27eb4b0ebc07 -# ╟─1da7e7ee-99a2-4e3e-9ffc-ee2b246f5da4 -# ╟─dc0fcf4b-ccb4-4e28-8ffb-268145008f23 -# ╟─ff760751-c7e6-4085-b96e-45e2732f6b5f -# ╟─7f4d4333-d66e-4ddb-8448-21db9bdc6c6d -# ╟─21d9f3cd-d68d-4f9d-9b3c-d31d7383c872 -# ╟─6f40fcb3-d8d1-4ade-a9ea-12fc58975f60 -# ╠═cc79a481-8bc9-442d-a129-a7f804ba0ccd -# ╟─6989f31e-2256-4532-b885-e5354ed9e99a -# ╟─39e255ea-1251-4b3b-a5bc-0cd65491020b -# ╟─1f8e149a-0c73-48b1-b9f1-74887c5fdeac -# ╠═99efeb26-41a8-4600-9aa8-8c9b88987ad7 -# ╟─196936b8-2dbf-4c56-8544-4a66a8b911af -# ╟─658bb88d-274f-4807-b18d-966eb9424e28 -# ╟─847d131e-f310-4d43-80b4-c120c9832eee -# ╠═0bac6f39-6683-4a08-bdad-31d232be36e7 -# ╟─b6b12ecb-995d-4fd2-82cf-0485c4ff1cf1 -# ╟─84fda41b-1309-4a58-be79-b517baf0814f -# ╠═e5636bd9-5e8e-46dd-9b6c-a66497d7df95 -# ╟─55e55e42-9186-4e04-a7f4-f433499b62b0 -# ╟─c4a84267-fc57-4a5d-b2ae-5db24896108d -# ╠═74c47afc-98ab-44a3-bc8b-2a104fbffb07 -# ╠═a2e21a23-3fc0-492a-9b27-cb7f307fcb1d -# ╠═decf04d3-3ce0-4d3d-a046-1989994ad874 -# ╠═ca807ea7-1980-4800-91f8-7b446923127a -# ╠═ed381482-fc40-4378-90b0-df86a474c864 -# ╠═a0f79b65-31d0-4faa-b87a-28335501ed61 -# ╠═baff69b5-5af7-4aaf-a1eb-846216deffc5 -# ╠═e7ad15ac-8976-4100-83ce-e964a6489605 -# ╠═0a6d4236-f1ac-474d-be98-3c2a53dd9a46 -# ╠═95417510-36db-451e-ba87-9faa23f7d412 -# ╠═4a7c619f-6ad6-43d6-b8e8-bddf4255bf17 -# ╠═022aad38-22e5-4d4d-a29a-3c4b900ab974 -# ╠═4d26800f-12b6-4ffe-b6e1-8db8834a8da9 -# ╠═c30f1640-42f4-4538-a5e0-ede64784bed1 -# ╠═e4f087f0-4c83-408d-a9a1-5bb1447a1da7 -# ╠═fab25481-21f9-490f-974f-6ea88177f68f -# ╠═9f2c259a-479d-483a-9cf5-c249b6049dd6 -# ╠═28a1addd-059c-419c-ba7b-1564b875f0e6 -# ╠═813cd252-659f-4ee5-89ad-d78a183928f3 -# ╟─76ec0b17-05ad-40bd-b922-6ad14e619013 -# ╟─9481ea51-b479-42a4-a5e8-03e0c4ea7387 -# ╠═1a97f1a6-8a0d-11ef-3a8e-8da68a63985b -# ╠═6d4d6baa-d160-47f3-bf7c-a9e0fabdf3c2 -# ╠═4aced393-1873-4fbf-b582-65a20346c64a -# ╠═31dff5a3-6912-42d1-a8dd-932bb618e21f -# ╠═3ab50cf5-da53-440c-9796-c9ee73b7ad86 -# ╠═8a5e88c4-b0a6-4848-b6d2-74bdbd960e7c -# ╠═8aaf540d-4e8b-499c-818f-3ce9f53a03cf -# ╠═6ef6f82a-5869-42c6-9111-ae125414ab1c -# ╠═0f49994b-f5fe-4083-b06e-2a4b99ed9200 -# ╠═1dd2589a-baad-415a-a842-9f3701812f8e -# ╠═d202a3b0-913d-4a63-a81c-cfb64f2e5176 -# ╠═7cb5d22d-4a2e-483e-a567-77c09fda306b diff --git a/experiments/Synth/notebooks/bma.png b/experiments/Synth/notebooks/bma.png deleted file mode 100644 index 3c27fb0..0000000 Binary files a/experiments/Synth/notebooks/bma.png and /dev/null differ diff --git a/experiments/Synth/notebooks/boolean_grammar.jl b/experiments/Synth/notebooks/boolean_grammar.jl deleted file mode 100644 index 00d267b..0000000 --- a/experiments/Synth/notebooks/boolean_grammar.jl +++ /dev/null @@ -1,881 +0,0 @@ -### A Pluto.jl notebook ### -# v0.19.46 - -using Markdown -using InteractiveUtils - -# ╔═╡ b6ce96ce-71cb-11ef-287e-4d1674490f7a -using Herb, HerbGrammar, SoleLogics - -# ╔═╡ 502cafdc-07b3-4aef-b6f2-507d3a93290e -n = 10 - -# ╔═╡ 57681b14-74da-42bb-a8c5-14b32879ab30 -a = ExplicitAlphabet(Atom.(1:n)) - -# ╔═╡ b109d4a3-f95d-4aca-950c-ae98bd7832e8 -op = [∧, ∨, ¬] - -# ╔═╡ f4eef80e-c0a4-4a7c-bf74-5c89da7e5187 -begin - g₁ = @cfgrammar begin end - for i = 1:n - add_rule!(g₁, :(A = Atom($i))) - end - add_rule!(g₁, :(B = (B ∧ B) | (B ∨ B) | ¬B | A)) -end - -# ╔═╡ bd1ecefd-2da8-432f-9d53-5aed437a2bf9 -r₁ = rand(RuleNode, g₁, :B, 3) - -# ╔═╡ fd339639-d5d6-4ce9-a4a2-4b2c77aad5ce -begin - state = TruthDict(1:n) - state[2] = false - state -end - -# ╔═╡ baf828e0-2b18-4302-8fa3-ba944a605d66 -state - -# ╔═╡ f356adbc-95b9-4616-b5cc-6eace10b0cb2 -eval(rulenode2expr(r₁, g₁)) - -# ╔═╡ ccc32418-4170-418a-9d23-0fb4453f9011 -interpret(eval(rulenode2expr(r₁, g₁)), state) - -# ╔═╡ 00000000-0000-0000-0000-000000000001 -PLUTO_PROJECT_TOML_CONTENTS = """ -[deps] -Herb = "c09c6b7f-4f63-49de-90d9-97a3563c0f4a" -HerbGrammar = "4ef9e186-2fe5-4b24-8de7-9f7291f24af7" -SoleLogics = "b002da8f-3cb3-4d91-bbe3-2953433912b5" - -[compat] -Herb = "~0.3.0" -HerbGrammar = "~0.3.0" -SoleLogics = "~0.9.5" -""" - -# ╔═╡ 00000000-0000-0000-0000-000000000002 -PLUTO_MANIFEST_TOML_CONTENTS = """ -# This file is machine-generated - editing it directly is not advised - -julia_version = "1.10.4" -manifest_format = "2.0" -project_hash = "27ed5179d330c86b4fb2eb044a68393036f985f9" - -[[deps.AbstractTrees]] -git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177" -uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" -version = "0.4.5" - -[[deps.ArgTools]] -uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" -version = "1.1.1" - -[[deps.ArnoldiMethod]] -deps = ["LinearAlgebra", "Random", "StaticArrays"] -git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6" -uuid = "ec485272-7323-5ecc-a04f-4719b315124d" -version = "0.4.0" - -[[deps.Artifacts]] -uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" - -[[deps.Base64]] -uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" - -[[deps.Bzip2_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "9e2a6b69137e6969bab0152632dcb3bc108c8bdd" -uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0" -version = "1.0.8+1" - 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-[[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.8.0+1" - -[[deps.libpng_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"] -git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4" -uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" -version = "1.6.43+1" - -[[deps.nghttp2_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.52.0+1" - -[[deps.p7zip_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -version = "17.4.0+2" - -[[deps.tectonic_jll]] -deps = ["Artifacts", "Fontconfig_jll", "FreeType2_jll", "Graphite2_jll", "HarfBuzz_ICU_jll", "HarfBuzz_jll", "ICU_jll", "JLLWrappers", "Libdl", "OpenSSL_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "54867b00af20c70b52a1f9c00043864d8b926a21" -uuid = "d7dd28d6-a5e6-559c-9131-7eb760cdacc5" -version = "0.13.1+0" -""" - -# ╔═╡ Cell order: -# ╠═b6ce96ce-71cb-11ef-287e-4d1674490f7a -# ╠═502cafdc-07b3-4aef-b6f2-507d3a93290e -# ╠═57681b14-74da-42bb-a8c5-14b32879ab30 -# ╠═b109d4a3-f95d-4aca-950c-ae98bd7832e8 -# ╠═f4eef80e-c0a4-4a7c-bf74-5c89da7e5187 -# ╠═bd1ecefd-2da8-432f-9d53-5aed437a2bf9 -# ╠═fd339639-d5d6-4ce9-a4a2-4b2c77aad5ce -# ╠═baf828e0-2b18-4302-8fa3-ba944a605d66 -# ╠═f356adbc-95b9-4616-b5cc-6eace10b0cb2 -# ╠═ccc32418-4170-418a-9d23-0fb4453f9011 -# ╟─00000000-0000-0000-0000-000000000001 -# ╟─00000000-0000-0000-0000-000000000002 diff --git a/experiments/Synth/notebooks/examine_traj.jl b/experiments/Synth/notebooks/examine_traj.jl deleted file mode 100644 index 2243bed..0000000 --- a/experiments/Synth/notebooks/examine_traj.jl +++ /dev/null @@ -1,145 +0,0 @@ -### A Pluto.jl notebook ### -# v0.20.1 - -using Markdown -using InteractiveUtils - -# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error). -macro bind(def, element) - quote - local iv = try - Base.loaded_modules[Base.PkgId( - Base.UUID("6e696c72-6542-2067-7265-42206c756150"), - "AbstractPlutoDingetjes", - )].Bonds.initial_value - catch - b -> missing - end - local el = $(esc(element)) - global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el) - el - end -end - -# ╔═╡ 172d1c64-9528-11ef-1455-69f3dc33e1c4 -using DrWatson - -# ╔═╡ 49710c05-a20a-473f-9569-0579a35b8093 -@quickactivate - -# ╔═╡ 1d98adb9-5beb-476c-84db-5e6ccaf8841b -using DataFrames - -# ╔═╡ a65b8474-8d7c-423a-9700-fda5a13e3622 -using JLD2 - -# ╔═╡ 15b53ea1-57cb-45a6-9f15-e18bd9b8df29 -using DynamicalSystems - -# ╔═╡ 7f760eb1-a3ab-4599-8d24-58696e67c36d -using Plots - -# ╔═╡ 77d6b0bc-cd28-46ed-b2f5-56eb94efc659 -using PlutoUI - -# ╔═╡ 6b843080-264a-4e30-a1fb-d05fb77ae526 -function split_state_space(trajectory::StateSpaceSet) - # split into pairs of input (all values) and output (changed value) - input_output_pairs_per_node = Dict{Int,Set{Tuple{Vector{Int},Int}}}() - for i = 1:length(trajectory)-1 - changed = findfirst(trajectory[i+1] .!= trajectory[i]) - # only proceed if there was a change - if !isnothing(changed) - - # in real data we don't know the direction of the transition - # was it from i -> i+1 or i+1 -> i, we only know that two - # states are adjacent, so for gathering data, we add both - # directions as IO pairs - # 1. state `i` and the new value of the single variable in - # the state that changed - # 2. state `i+1` and the previous value of the single variable - # in the state that changed - - new_value = trajectory[i+1][changed] - old_value = trajectory[i][changed] - existing_pairs = - get(input_output_pairs_per_node, changed, Set{Tuple{Vector{Int},Int}}()) - push!( - existing_pairs, - (trajectory[i], new_value), # 1 - (trajectory[i+1], old_value), # 2 - ) - input_output_pairs_per_node[changed] = existing_pairs - end - end - - return input_output_pairs_per_node -end - -# ╔═╡ 85379de4-3258-4471-b43c-75c65aea0b10 -fname = "id=189_iterations=1000_n_trajectories=1000.jld2" - -# ╔═╡ f2c8a3fa-61b5-4f39-b5f6-3329eba23ede -pname = joinpath(datadir("sims", "biodivine_trajectories"), fname) - -# ╔═╡ 59dafbbe-086d-4fe6-b01c-e13ab8dc7d80 -res = load(pname) - -# ╔═╡ 1ce04619-14b1-4a4b-bc65-40dfdcae2c74 -anim = @animate for i in eachindex(res["trajectories"]) - heatmap(Matrix(res["trajectories"][i])) -end - -# ╔═╡ b36271d5-f908-4e65-b0fa-9175e8c2416c -# gif(anim) - -# ╔═╡ 436fc3d5-fbf5-46b4-a6fd-3f8c97a00a47 -traj = res["trajectories"] - -# ╔═╡ 25966e19-4f35-4b3e-b60a-238cc182fbc9 -@bind ind Slider(1:1000) - -# ╔═╡ de871eaa-0991-4b3d-8c81-fd8afb284df7 -heatmap(Matrix(res["trajectories"][ind])) - -# ╔═╡ fe295564-151a-4325-9c4a-f47963ede398 -split_applied = [split_state_space(t) for t in traj] - -# ╔═╡ 17f352fc-6f4e-4d7d-ad7a-d6d2ac049ada -begin - name = tempname() - name *= ".jld2" - save(name, @strdict split_applied) -end - -# ╔═╡ 947a965d-54f5-40e3-b703-4a18bf671d9c -filesize(name) * 1e-6 - -# ╔═╡ 3c443200-79f9-4288-8989-6c06408240d5 -filesize(pname) * 1e-6 - -# ╔═╡ 5ca0375d-d325-4831-85c4-17e871dc5a8b -name - -# ╔═╡ Cell order: -# ╠═172d1c64-9528-11ef-1455-69f3dc33e1c4 -# ╠═49710c05-a20a-473f-9569-0579a35b8093 -# ╠═1d98adb9-5beb-476c-84db-5e6ccaf8841b -# ╠═a65b8474-8d7c-423a-9700-fda5a13e3622 -# ╠═15b53ea1-57cb-45a6-9f15-e18bd9b8df29 -# ╠═7f760eb1-a3ab-4599-8d24-58696e67c36d -# ╠═6b843080-264a-4e30-a1fb-d05fb77ae526 -# ╠═85379de4-3258-4471-b43c-75c65aea0b10 -# ╠═f2c8a3fa-61b5-4f39-b5f6-3329eba23ede -# ╠═59dafbbe-086d-4fe6-b01c-e13ab8dc7d80 -# ╠═1ce04619-14b1-4a4b-bc65-40dfdcae2c74 -# ╠═b36271d5-f908-4e65-b0fa-9175e8c2416c -# ╠═436fc3d5-fbf5-46b4-a6fd-3f8c97a00a47 -# ╠═77d6b0bc-cd28-46ed-b2f5-56eb94efc659 -# ╠═25966e19-4f35-4b3e-b60a-238cc182fbc9 -# ╠═de871eaa-0991-4b3d-8c81-fd8afb284df7 -# ╠═fe295564-151a-4325-9c4a-f47963ede398 -# ╠═17f352fc-6f4e-4d7d-ad7a-d6d2ac049ada -# ╠═947a965d-54f5-40e3-b703-4a18bf671d9c -# ╠═3c443200-79f9-4288-8989-6c06408240d5 -# ╠═5ca0375d-d325-4831-85c4-17e871dc5a8b diff --git a/experiments/Synth/notebooks/exploring_real_gene_data.jl b/experiments/Synth/notebooks/exploring_real_gene_data.jl deleted file mode 100644 index e0d1c29..0000000 --- a/experiments/Synth/notebooks/exploring_real_gene_data.jl +++ /dev/null @@ -1,1955 +0,0 @@ -### A Pluto.jl notebook ### -# v0.20.1 - -using Markdown -using InteractiveUtils - -# ╔═╡ 930e6384-7990-11ef-21cd-91d65824df6c -using XLSX, DataFrames - -# ╔═╡ 0bd38cff-a42e-4565-bfa9-ced44cf8f8df -using StatsBase - -# ╔═╡ cd53eca5-16c9-4e53-9dcb-bb7e4ba889c8 -using StatsPlots - -# ╔═╡ 7e2bc2d2-7642-4185-8abd-65403feba9fa -using Plots - -# ╔═╡ 4c569c51-e22b-41c0-8b8e-856c8d6e490a -using Clustering - -# ╔═╡ 86b9297d-a84c-4e88-b80f-31751ab697ba -using Distances - -# ╔═╡ d1ca22b2-78a5-4281-831b-fecde86a788e -using Graphs, GraphRecipes - -# ╔═╡ 8ad47ec9-6a67-4844-915c-299d0dbd0cc9 -using MarkdownLiteral - -# ╔═╡ ddb6544f-5871-4013-b5ff-81ab8ebbaca4 -begin - using PlutoTeachingTools, HypertextLiteral, ShortCodes - FootnotesNumbered() -end - -# ╔═╡ fa63d2a3-1d4b-48fe-9bea-99b6eae3903f -# plotly() -gr() - -# ╔═╡ bc84a251-bbe7-410b-b035-2ed7cc225f4e -xlsx_path = expanduser("~/Downloads/41587_2015_BFnbt3154_MOESM4_ESM.xlsx") - -# ╔═╡ e2e40050-e776-4aff-b8e8-dd983d87a392 -binary_xlsx_path = expanduser("~/Downloads/41587_2015_BFnbt3154_MOESM7_ESM.xlsx") - -# ╔═╡ 015f3d93-19bb-46bf-a94c-ff6e29caa45c -paper_binary = DataFrame(XLSX.readtable(binary_xlsx_path, "Table_S3.csv")) - -# ╔═╡ ad59a810-397f-461c-b9b0-be5009299a80 -raw_data = DataFrame(XLSX.readtable(xlsx_path, "Raw Ct values")) - -# ╔═╡ e078e04f-f228-42a6-81d7-77609a6e9252 -dct_data = DataFrame(XLSX.readtable(xlsx_path, "dCt_values.txt")) - -# ╔═╡ 400ab810-7748-4c33-ab30-c82a0fde5de4 -only_activation = - convert.(Float64, dct_data[:, Not(:Cell, :Eif2b1, :Mrpl19, :Polr2a, :Ubc)]) - -# ╔═╡ f46c4081-b6b4-4b45-8a9c-0ff829cdc346 -float_matrix = Matrix(only_activation) - -# ╔═╡ e8f7386f-d622-4b1b-a0de-d9657137ff77 -# dist_row = pairwise(hamming, float_matrix, dims=1) - -# ╔═╡ 4b2f9ac9-45af-4e32-b665-9768c97d8fff -# dist_col = pairwise(hamming, float_matrix, dims=2) - -# ╔═╡ 73dcffc5-4da8-457c-884f-905d926dca0e -dist_cols = 1 .- corspearman(float_matrix) - -# ╔═╡ 2a1f6238-1cf9-46e3-9145-98f6fadf8731 -dist_rows = 1 .- corspearman(transpose(float_matrix)) - -# ╔═╡ 705c4209-e9ea-48a2-9c52-8d9023e03f56 -clust_rows = hclust(dist_rows, linkage = :complete, branchorder = :barjoseph) - -# ╔═╡ 5632176d-2642-4b0d-bb22-c1fb3cd7b30a -clust_cols = hclust(dist_cols, linkage = :complete, branchorder = :barjoseph) - -# ╔═╡ a5a03630-541b-4bb3-9efa-cb784c99ae9e -begin - l = Plots.grid(2, 2, heights = [0.2, 0.8], widths = [0.2, 0.8]) - plot( - layout = l, - plot(ticks = nothing, border = :none), - plot(clust_rows, linewidth = 0.25, ticks = false), - plot( - clust_cols, - xrotation = 90, - orientation = :horizontal, - xlim = (0, 2), - ymirror = true, - xflip = true, - yticks = (1:length(clust_rows.order), names(only_activation)[clust_cols.order]), - xticks = false, - ), - plot( - transpose(float_matrix[clust_rows.order, clust_cols.order]), - st = :heatmap, - yticks = false, - legend = false, - ticks = false, - ), - size = (1200, 1200), - ) -end - -# ╔═╡ fb88375b-1265-489d-b519-97c4bfca03fc -threshold = -3.05 - -# ╔═╡ 85da8e36-af8c-4361-84d5-22858dec552d -binary = dct_data[:, Not(:Cell)] .<= threshold - -# ╔═╡ 77e4a71c-7e6e-4628-b1da-8cd7d9b7bc60 -binary_matrix = Matrix(binary) - -# ╔═╡ ec452271-2407-4722-b289-485d4db85288 -pairwise(hamming, binary_matrix, binary_matrix) - -# ╔═╡ 3d4954d9-69f1-479d-a4e0-bd3dc78b4ffa -md""" -As mentionoed in [^decoding], the bookkeeping proteins are: $(join(names(binary[:, describe(binary, :nuniqueall).nuniqueall .== 1]), ", ")). These have constant values expected, so all experiments where they were not constant were discarded. -""" - -# ╔═╡ 1ae60a74-82c6-4027-b2e1-14bbdffec24a -md""" -Here, we remove those bookeeping/constant-valued columns. -""" - -# ╔═╡ ed211d66-4d47-413a-9069-3ce84b9aa30b -binary_nonconst = binary[:, describe(binary, :nuniqueall).nuniqueall.!=1]; - -# ╔═╡ fe7a5523-ecbc-43ac-a821-1d1d2a655c6a -states = unique(binary_nonconst) - -# ╔═╡ 45b00bc1-31d7-442b-bac9-f6d3093aee8f -state_pairs = - let d = view(pairwise(hamming, Matrix(paper_binary), dims = 1), 1:nrow(paper_binary), :) - state_pairs = Edge[] - for (i, v) in pairs(IndexStyle(d), d) - if i[1] - i[2] < 0 - if v == 1 - append!(state_pairs, [Edge(Tuple(i))]) - end - end - end - state_pairs - end - -# ╔═╡ b7b094cf-fecc-4901-a1bc-b638822588d5 -state_space = SimpleGraphFromIterator(state_pairs) - -# ╔═╡ 2febf7d2-0360-4540-98a1-405e7498dc60 -collect(edges(state_space)) - -# ╔═╡ 2b8f5956-e003-427d-bddb-2b7537e82ad2 -plot(state_space, curves = false, method = :circle) - -# ╔═╡ 34fb5ab5-c2c3-4511-ac8e-b2783ed751b6 -pairwise(hamming, Matrix(paper_binary), dims = 1) - -# ╔═╡ e6fca700-5a9e-4ebe-9416-ebc51434f415 -# md""" -# [^decoding]: See below. Interpolation broken, reported in [PlutoTeachingTools.jl#57](https://github.com/JuliaPluto/PlutoTeachingTools.jl/issues/57) -# $(DOI("10.1038/nbt.3154")) -# """ - -# ╔═╡ c9250da3-a1a8-4f31-b4db-c1484187d1b6 -# md""" -# [^decoding]: $(DOI("10.1038/nbt.3154")) -# """ - -# ╔═╡ e492f43a-3ad7-4f19-bda5-4218d3e8f8a2 -MarkdownLiteral.@markdown("[^article]: $(DOI("10.1038/nbt.3154"))") - -# ╔═╡ 67327fd0-9f28-4a5b-bea5-9502c67f886d -DOI("10.1038/nbt.3154") - -# ╔═╡ 1bd609c5-a47b-4747-8b64-97aabb2cd06e -md""" -Buettner, Florian; Diamanti, Evangelia; Fisher, Jasmin; Göttgens, Berthold; Haghverdi, Laleh; Jawaid, Wajid; Kouskoff, Valerie; Lilly, Andrew J; Macaulay, Iain; Moignard, Victoria; Nishikawa, Shin-Ichi; Piterman, Nir; Tanaka, Yosuke; Theis, Fabian; Wilkinson, Adam C; Woodhouse, Steven *Decoding The Regulatory Network Of Early Blood Development From Single-Cell Gene Expression Measurements*, Nature Biotechnology (2015) 10/f64rrc, cited by 337 -""" - -# ╔═╡ 3aca30ea-8cf5-40f5-b65d-b1bbe1dc8c46 -md""" -$(1+1) -""" - 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-[[deps.Xorg_xcb_util_keysyms_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] -git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00" -uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7" -version = "0.4.0+1" - -[[deps.Xorg_xcb_util_renderutil_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] -git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e" -uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e" -version = "0.3.9+1" - -[[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" - -[[deps.Xorg_xkbcomp_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxkbfile_jll"] -git-tree-sha1 = "330f955bc41bb8f5270a369c473fc4a5a4e4d3cb" -uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" -version = "1.4.6+0" - -[[deps.Xorg_xkeyboard_config_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xkbcomp_jll"] -git-tree-sha1 = "691634e5453ad362044e2ad653e79f3ee3bb98c3" -uuid = "33bec58e-1273-512f-9401-5d533626f822" -version = "2.39.0+0" - -[[deps.Xorg_xtrans_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77" -uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" -version = "1.5.0+0" - -[[deps.ZipArchives]] -deps = ["ArgCheck", "CodecInflate64", "CodecZlib", "InputBuffers", "PrecompileTools", "TranscodingStreams", "Zlib_jll"] -git-tree-sha1 = "c77707ebf7aad0caa57ad7b561b4f89b0caefc73" -uuid = "49080126-0e18-4c2a-b176-c102e4b3760c" -version = "2.3.0" - -[[deps.ZipFile]] -deps = ["Libdl", "Printf", "Zlib_jll"] -git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a" -uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" -version = "0.10.1" - -[[deps.Zlib_jll]] -deps = ["Libdl"] -uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -version = "1.2.13+1" - -[[deps.Zstd_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "e678132f07ddb5bfa46857f0d7620fb9be675d3b" -uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" -version = "1.5.6+0" - -[[deps.fzf_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "936081b536ae4aa65415d869287d43ef3cb576b2" -uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" -version = "0.53.0+0" - -[[deps.libaom_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "1827acba325fdcdf1d2647fc8d5301dd9ba43a9d" -uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" -version = "3.9.0+0" - -[[deps.libass_jll]] -deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Zlib_jll"] -git-tree-sha1 = "e17c115d55c5fbb7e52ebedb427a0dca79d4484e" -uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" -version = "0.15.2+0" - -[[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.8.0+1" - -[[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.libfdk_aac_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "8a22cf860a7d27e4f3498a0fe0811a7957badb38" -uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" -version = "2.0.3+0" - -[[deps.libpng_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"] -git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4" -uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" -version = "1.6.43+1" - -[[deps.libvorbis_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] -git-tree-sha1 = "490376214c4721cdaca654041f635213c6165cb3" -uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" -version = "1.3.7+2" - -[[deps.nghttp2_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.52.0+1" - -[[deps.oneTBB_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "7d0ea0f4895ef2f5cb83645fa689e52cb55cf493" -uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e" -version = "2021.12.0+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", "Pkg"] -git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" -uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" -version = "2021.5.5+0" - -[[deps.x265_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" -uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" -version = "3.5.0+0" - -[[deps.xkbcommon_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] -git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37" -uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "1.4.1+1" -""" - -# ╔═╡ Cell order: -# ╠═930e6384-7990-11ef-21cd-91d65824df6c -# ╠═0bd38cff-a42e-4565-bfa9-ced44cf8f8df -# ╠═cd53eca5-16c9-4e53-9dcb-bb7e4ba889c8 -# ╠═7e2bc2d2-7642-4185-8abd-65403feba9fa -# ╠═fa63d2a3-1d4b-48fe-9bea-99b6eae3903f -# ╠═bc84a251-bbe7-410b-b035-2ed7cc225f4e -# ╠═e2e40050-e776-4aff-b8e8-dd983d87a392 -# ╠═015f3d93-19bb-46bf-a94c-ff6e29caa45c -# ╠═ad59a810-397f-461c-b9b0-be5009299a80 -# ╠═e078e04f-f228-42a6-81d7-77609a6e9252 -# ╠═4c569c51-e22b-41c0-8b8e-856c8d6e490a -# ╠═400ab810-7748-4c33-ab30-c82a0fde5de4 -# ╠═f46c4081-b6b4-4b45-8a9c-0ff829cdc346 -# ╠═e8f7386f-d622-4b1b-a0de-d9657137ff77 -# ╠═4b2f9ac9-45af-4e32-b665-9768c97d8fff -# ╠═73dcffc5-4da8-457c-884f-905d926dca0e -# ╠═2a1f6238-1cf9-46e3-9145-98f6fadf8731 -# ╠═705c4209-e9ea-48a2-9c52-8d9023e03f56 -# ╠═5632176d-2642-4b0d-bb22-c1fb3cd7b30a -# ╠═a5a03630-541b-4bb3-9efa-cb784c99ae9e -# ╠═fb88375b-1265-489d-b519-97c4bfca03fc -# ╠═85da8e36-af8c-4361-84d5-22858dec552d -# ╠═77e4a71c-7e6e-4628-b1da-8cd7d9b7bc60 -# ╠═86b9297d-a84c-4e88-b80f-31751ab697ba -# ╠═ec452271-2407-4722-b289-485d4db85288 -# ╠═3d4954d9-69f1-479d-a4e0-bd3dc78b4ffa -# ╟─1ae60a74-82c6-4027-b2e1-14bbdffec24a -# ╠═ed211d66-4d47-413a-9069-3ce84b9aa30b -# ╠═fe7a5523-ecbc-43ac-a821-1d1d2a655c6a -# ╠═45b00bc1-31d7-442b-bac9-f6d3093aee8f -# ╠═d1ca22b2-78a5-4281-831b-fecde86a788e -# ╠═b7b094cf-fecc-4901-a1bc-b638822588d5 -# ╠═2febf7d2-0360-4540-98a1-405e7498dc60 -# ╠═2b8f5956-e003-427d-bddb-2b7537e82ad2 -# ╠═34fb5ab5-c2c3-4511-ac8e-b2783ed751b6 -# ╠═e6fca700-5a9e-4ebe-9416-ebc51434f415 -# ╠═c9250da3-a1a8-4f31-b4db-c1484187d1b6 -# ╠═8ad47ec9-6a67-4844-915c-299d0dbd0cc9 -# ╠═e492f43a-3ad7-4f19-bda5-4218d3e8f8a2 -# ╠═67327fd0-9f28-4a5b-bea5-9502c67f886d -# ╠═1bd609c5-a47b-4747-8b64-97aabb2cd06e -# ╠═ddb6544f-5871-4013-b5ff-81ab8ebbaca4 -# ╠═3aca30ea-8cf5-40f5-b65d-b1bbe1dc8c46 -# ╠═2a9edf82-9394-47da-b0f3-b63592649786 -# ╠═15467e96-4514-4fe1-aa26-3f78534fd844 -# ╠═83a341a3-8fd2-4d27-a526-a16080e41fa8 -# ╟─00000000-0000-0000-0000-000000000001 -# ╟─00000000-0000-0000-0000-000000000002 diff --git a/experiments/Synth/notebooks/exploring_synth_bn.jl b/experiments/Synth/notebooks/exploring_synth_bn.jl deleted file mode 100644 index 30db61b..0000000 --- a/experiments/Synth/notebooks/exploring_synth_bn.jl +++ /dev/null @@ -1,117 +0,0 @@ -### A Pluto.jl notebook ### -# v0.19.47 - -using Markdown -using InteractiveUtils - -# ╔═╡ 70b96736-797d-11ef-2813-314ac85c3364 -using DrWatson - -# ╔═╡ cbd7f05b-bd2d-4fd1-a178-efe01459e45f -@quickactivate - -# ╔═╡ ab9b3cf7-8faf-4c24-936c-428132697f91 -using DataFrames, Herb, SoleLogics - -# ╔═╡ 99e6781b-125c-4e27-9148-c44e8bb3519e -using MetaGraphsNext, - DynamicalSystems, GraphDynamicalSystems, StatsPlots, GraphRecipes, Plots - -# ╔═╡ ed88fb56-f1e7-4c9b-bb8f-639e40e74375 -networks = collect_results(datadir("sims", "specs")); - -# ╔═╡ 8466f8d0-3cd3-4dbe-ab1d-ff591a0bc3dd -networks_by_seed = groupby(networks, :seed); - -# ╔═╡ 8bd06f89-5c7c-4a9d-8c9e-6f81f1a8cfa0 -names(networks_by_seed) - -# ╔═╡ 4fbf88ff-86ec-4447-859a-7e3c370b5e71 -bn = networks[networks.seed.==1, :].bn[1] - -# ╔═╡ b618790b-db83-4153-9737-e1a76773608e -bn.vertex_properties - -# ╔═╡ cb9af591-329d-43dd-aa74-688fc888766c -graphplot( - bn.graph, - method = :circular, - # names=[String(val[2]) for val in values(bn.vertex_properties)] -) - -# ╔═╡ 70d17571-96a2-4cc4-9059-3c203e32b3c2 -results = collect_results(datadir("exp_raw", "cnf_search")); - -# ╔═╡ 1cb1376c-967c-4f71-9134-2654e255e179 -by_grammar = groupby(results, [:grammar_type, :seed, :node]); - -# ╔═╡ c478bf1a-451d-4469-8bd2-88a3a375907f -function get_found_after(results_node, expected_func) - for (i, r) in enumerate(eachrow(results_node)) - (f, s) = r[1] - if normalize(f) == normalize(expected_func) - return i - end - end - - return Inf -end - -# ╔═╡ 49c919a1-1bdf-497c-bdaa-de502711c0ef -dnf_node_1_ex_sc = by_grammar[("cnf", 0, 2)][!, :exprs_and_scores][1] - -# ╔═╡ e22e7263-95c9-43cb-98b2-09dd3dba4b5b -bn_0_node_1 = networks_by_seed[(0,)].bn[1][2] - -# ╔═╡ 36d246c7-edf1-4cf9-b4fd-50646f8387a1 -get_found_after(dnf_node_1_ex_sc, bn_0_node_1) - -# ╔═╡ 5e95a352-d09b-4996-87af-1977ea02717e -# @df dnf bar( -# :node, -# :found_after, -# xlims = [0, 12], -# xlabel = "Node In Network", -# ylabel = "First Solution at # Iterations", -# ) - -# ╔═╡ 352515cd-c7fc-43bb-8761-33d93521e8ad -cnf_seed_1 = by_grammar[("cnf", 1, 1)]; - -# ╔═╡ 4499a2c8-d6a5-4417-b4ac-ea5e30289167 -@df cnf_seed_1 bar( - :node, - :node, - xlims = [0, 12], - xlabel = "Node In Network", - ylabel = "First Solution at # Iterations", -) - -# ╔═╡ fe160a77-7064-4850-be33-7053911d8706 -dnf.all_ex - -# ╔═╡ 3e06906d-913c-4de0-8091-05f60347d4fe -cnf_seed_1.all_ex - -# ╔═╡ Cell order: -# ╠═70b96736-797d-11ef-2813-314ac85c3364 -# ╠═cbd7f05b-bd2d-4fd1-a178-efe01459e45f -# ╠═ab9b3cf7-8faf-4c24-936c-428132697f91 -# ╠═99e6781b-125c-4e27-9148-c44e8bb3519e -# ╠═ed88fb56-f1e7-4c9b-bb8f-639e40e74375 -# ╠═8466f8d0-3cd3-4dbe-ab1d-ff591a0bc3dd -# ╠═8bd06f89-5c7c-4a9d-8c9e-6f81f1a8cfa0 -# ╠═4fbf88ff-86ec-4447-859a-7e3c370b5e71 -# ╠═b618790b-db83-4153-9737-e1a76773608e -# ╠═cb9af591-329d-43dd-aa74-688fc888766c -# ╠═70d17571-96a2-4cc4-9059-3c203e32b3c2 -# ╠═1cb1376c-967c-4f71-9134-2654e255e179 -# ╠═c478bf1a-451d-4469-8bd2-88a3a375907f -# ╠═49c919a1-1bdf-497c-bdaa-de502711c0ef -# ╠═e22e7263-95c9-43cb-98b2-09dd3dba4b5b -# ╠═36d246c7-edf1-4cf9-b4fd-50646f8387a1 -# ╠═5e95a352-d09b-4996-87af-1977ea02717e -# ╠═4499a2c8-d6a5-4417-b4ac-ea5e30289167 -# ╠═352515cd-c7fc-43bb-8761-33d93521e8ad -# ╠═fe160a77-7064-4850-be33-7053911d8706 -# ╠═3e06906d-913c-4de0-8091-05f60347d4fe diff --git a/experiments/Synth/notebooks/make_asp.jl b/experiments/Synth/notebooks/make_asp.jl deleted file mode 100644 index 9407527..0000000 --- a/experiments/Synth/notebooks/make_asp.jl +++ /dev/null @@ -1,705 +0,0 @@ -### A Pluto.jl notebook ### -# v0.20.8 - -using Markdown -using InteractiveUtils - -# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error). -macro bind(def, element) - #! format: off - return quote - local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end - local el = $(esc(element)) - global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el) - el - end - #! format: on -end - -# ╔═╡ b402713a-02a9-11f0-1bca-69b96cd9c786 -using DrWatson - -# ╔═╡ ca420cdc-208a-4341-8409-b2e826dcf9d7 -begin - quickactivate(pwd()) - using Synth - using ProgressMeter, DataFrames, HerbSearch, GraphDynamicalSystems, Random - using MetaGraphsNext: labels - using Statistics: quantile - using DynamicalSystems - using Clingo_jll - using Plots - using Graphs - using GraphRecipes -end - -# ╔═╡ 73a912bb-3f03-4190-a4b8-7d3352b541ff -using BenchmarkTools - -# ╔═╡ df97a629-608b-45d0-815d-0a6c1d37d95c -using PlutoUI - -# ╔═╡ f09edd39-ffeb-4850-b29e-b38565273ff5 -md""" -## Creating an ASP Model for the Local Solution Assignment Problem - -As input to the problem, we need to define the following ingredients: - -- `entity(...).` -- `state(...).` -- `solution(...).` -- `steady(...).` - -as well as mappings between ingredients: - -- `belongs_to(solution, entity).` -- `connects(solution, state, state).` - -### Entity Definitions - -The `entity` definition is easy. Just a list of the entities. -""" - -# ╔═╡ 701cca9d-f8de-4160-8f5d-c1ce30d31525 -md""" -### State Definitions - -For the local synthesis problems, we select a set of trajectories to synthesize from. The result of the local synthesis problem gives us information about which candidate solution connects which pairs of adjacent states. -""" - -# ╔═╡ 690734ed-bdbf-4635-a4d2-37500fcea3cf -md""" -### Solution Definitions - -Currently, we are storing the candidate solutions (expressions) and their scores in a 4-tuple: - -> `(expression, score, per-transition-success, id)` - -We extract the ID, and create a unique ID for the solution by prepending the candidate name. -""" - -# ╔═╡ 51f9d1b9-304e-417f-9925-b572a90e278e -md""" -!!! warning - I'm currently pruning too much, so this is not working. Solving for an assignment is still fast (<1s) so for now this doesn't matter. -""" - -# ╔═╡ f298831c-8954-4957-a2b9-92f7b0cf9ca7 -md""" -### Steady States - -To mark which states are steady, we need to load the original Boolean Network model and extract them. -""" - -# ╔═╡ b8265bb9-83d4-4bfd-9480-a9b55d78495e -begin - model_df = collect_results(datadir("src_parsed", "biodivine_benchmark_as_metagraphs");); - path2id = path -> parse(Int, splitext(basename(path))[1]) - model_df.ID = path2id.(model_df.path) -end - -# ╔═╡ 437a480c-e63d-40f2-9d01-dd95275a333c -@assert model_df.ID[1] == 7 # assuming we only have the results for model 7 loaded - -# ╔═╡ 7e86270e-0faf-4b05-b5b2-f446d62e3320 -original_model = model_df.metagraph_model[1] - -# ╔═╡ 003f7d18-1801-4eda-9092-1d8685e41da6 -original_aeon = """ -\$v_Coup_fti: !(v_Fgf8 | v_Sp8) | !(v_Sp8 | v_Fgf8) -\$v_Emx2: v_Coup_fti & !(v_Fgf8 | v_Sp8 | v_Pax6) -\$v_Fgf8: v_Fgf8 & v_Sp8 & !v_Emx2 -\$v_Pax6: v_Sp8 & !(v_Emx2 | v_Coup_fti) -\$v_Sp8: v_Fgf8 & !v_Emx2 -""" |> (x -> replace(x, "v_" => "", "\$" => "", "!" => "¬", "|" => "∨", "&" => "∧", "\n" => "\n")) - -# ╔═╡ cf9bb61c-7892-4b0b-91fe-e9c437be849d -Markdown.parse(""" -```clingo -$original_aeon -``` -""") - -# ╔═╡ e699ff65-d7cc-4a9b-80b8-1fe4b17bb6ea -plot( - reverse(original_model.graph); - names=Dict([k => v.value[3:end] for (k, v) in original_model.vertex_labels]), - nodecolor=:lightblue -) - -# ╔═╡ 38bcaaaf-5ddb-4536-b270-4b93caba66c2 -(_, target_basins) = get_basins_bn(original_model) - -# ╔═╡ 17aa2324-3b02-47c9-8c91-9bbdd4c3cb4a -steady_states = Iterators.flatten(values(target_basins)) |> collect - -# ╔═╡ 7d8c46e2-c03a-4e50-ab5a-336b58b28115 -for s in steady_states - for x in s - print("[$x], ") - end - println() -end - -# ╔═╡ 749b6a19-a723-4df4-a37f-4d2d30f8d4e7 -md""" ---- - -### Belongs-To Definitions - -This might seem useless for now, because the entity is also in the solution name, but it is necessary with the way the model is currently defined. Otherwise, Clingo would not know the mapping. -""" - -# ╔═╡ 50f0fcd3-e181-4d71-b833-8b67886806ab -md""" -### Connects Definitions - -From our 4-tuple of results, we now need the `per-transition-success`, which looks like a vector of `BitVectors` (maybe should be a `BitArray`?). - -> ``` -> BitVector[[1, 0], [0, 1], [0, 1], [1, 0], [1, 0]] -> ``` - -The above `BitVectors` correspond to a solution that connects the first IO example from left (input) to right (output), the second from right to left, and so on. - -So, to create the `connects(solution, state, state)` needs a map from the pairs to -""" - -# ╔═╡ 7a58b03d-41d0-4380-85d2-231646807c5e -md""" -## Model Construction - -Now that we have all of the ingredients, we can build the model and send it to Clingo to solve. - -### Extra Checks - -First, some checks to make sure the definitions above are correct-ish. - -1. Are the entities lowercase, and only consisting of letters and numbers? Do they start with a letter? -""" - -# ╔═╡ 458d40a7-b695-4307-aaae-b84ec75b1f56 -md""" -2. Is there at least one state? -""" - -# ╔═╡ 73360b3d-d702-4d82-9923-6aa7dccc08cf -md""" -3. Do all of the solution names start with one of the entity names from `vertex_names`? Are they followed by digits? Are they split with an underscore? -""" - -# ╔═╡ 0dce9e1f-ad34-4a6a-b467-9517ee980f63 -md""" -4. Does the pruning result in the same number of solutions as the "fingerprints" of solutions from their `per-transition-success` information? -""" - -# ╔═╡ 32009cc3-b6de-4797-bc32-6bec42964d30 -md""" -5. Does the pruning result in the same number of solutions `per-transition-success` information **per entity**? -""" - -# ╔═╡ f5f4843c-eb6d-47e1-91c2-f92a817ce1e4 -md""" -### Build Model File -""" - -# ╔═╡ 1141d91e-ee98-4740-9765-ce1aa4f178cf -model_def = """ - -% For every entity, assign one solution. That solution must belong to the entity. -1 { assign(SOLUTION, ENTITY) : belongs_to(SOLUTION, ENTITY) } 1 :- entity(ENTITY). - -% Edges connect states X and Y if a solution connects them and that solution is assigned -edge(X, Y) :- connects(SOLUTION, X, Y), assign(SOLUTION, _). -% A self-edge exists if X is steady and is a state -edge(X, X) :- steady(X), state(X). -% A path exists between X and Z if a an edge directly connects them -path(X, Z) :- edge(X, Z). -% A path from X to Z also exists if an edge connects X to an intermediate Y -% for which there exists a path connecting Y to Z -path(X, Z) :- edge(X, Y), path(Y, Z). - -% :- state(X), steady(Y), not path(X, Y). -% There is a conflict if there is a non-steady state X and a steady state Y -% where there is not a path between X and Y -% conflict(X, Y) -:- state(X), not steady(X), steady(Y), not path(X, Y), path(Y, X). -% all states should have one outgoing edge -% conflict(X) -:- state(X), not path(X, _). -:- state(Y), steady(Y), state(X), not steady(X), path(Y, X). - -% Output -% #show entity /1. -#show assign /2. -%#show edge /2. -%#show conflict /1. -""" - -# ╔═╡ 5207cff2-6f14-4ced-a7c3-bcb5e899b83a -md""" -## Run Model -""" - -# ╔═╡ 3010c862-fb99-4993-9109-d90b1ab04e46 -md""" -Filter? $(@bind filtered PlutoUI.CheckBox(default=true)) -""" - -# ╔═╡ 02155e62-04d9-4c66-b29a-e4bb53e467d2 -md""" -### Examine Solution - -One solution from the unfiltered model is - -> `assign(sp8_3210,sp8) assign(emx2_1128,emx2) assign(coupfti_3218,coupfti) assign(pax6_9228,pax6) assign(fgf8_5146,fgf8)` -""" - -# ╔═╡ 26153e85-5a49-4376-a9ad-0e55ad6d1329 -assignments = [ - "sp8_3210", - "emx2_1128", - "coupfti_3218", - "pax6_9228", - "fgf8_5146" -] - -# ╔═╡ a434481a-e609-4a6d-a9cb-f0136672afff - - -# ╔═╡ 1de6aafa-9012-4027-a754-7e3ca811fb6a -md""" -All of the assignments should be in the solution names list. -""" - -# ╔═╡ 1337307b-163c-4eb2-8d1f-702637837d01 -md""" -What is their index in the original solutions? We need this to examine their transitions. -""" - -# ╔═╡ b06e9728-6482-4082-baf5-d3ad63f02b16 -first_transition = [ - BitVector([true, true]), - BitVector([true, true]), - BitVector([true, true]), - BitVector([true, true]), - BitVector([true, true]), -] - -# ╔═╡ a5e7fab3-8e13-4e88-a452-72ac0617055a -md""" -### Examine Solution Edges -""" - -# ╔═╡ ba1a2fcd-f825-4d06-aecf-486ea3584c5b -res_filtered = "edge(20,20) edge(10,10) assign(sp8_1409,sp8) assign(emx2_1393,emx2) assign(coupfti_1,coupfti) assign(pax6_6325,pax6) assign(fgf8_20,fgf8) edge(1,2) edge(4,3) edge(6,5) edge(8,7) edge(10,9) edge(11,12) edge(10,13) edge(3,14) edge(15,8) edge(6,11) edge(10,16) edge(2,14) edge(13,17) edge(4,18) edge(15,13) edge(12,1) edge(19,9) edge(20,2) edge(21,15) edge(16,18) conflict(1,20) conflict(2,20) conflict(3,20) conflict(4,20) conflict(5,20) conflict(6,20) conflict(7,20) conflict(8,20) conflict(9,20) conflict(11,20) conflict(12,20) conflict(13,20) conflict(14,20) conflict(15,20) conflict(16,20) conflict(17,20) conflict(18,20) conflict(19,20) conflict(21,20) conflict(1,10) conflict(2,10) conflict(3,10) conflict(4,10) conflict(5,10) conflict(6,10) conflict(7,10) conflict(8,10) conflict(9,10) conflict(11,10) conflict(12,10) conflict(13,10) conflict(14,10) conflict(15,10) conflict(16,10) conflict(17,10) conflict(18,10) conflict(19,10) conflict(21,10)" - -# ╔═╡ 6feceb12-f887-4fed-a9a1-c085267188b5 -res_sat = "edge(20,20) edge(10,10) assign(sp8_2282,sp8) assign(emx2_6156,emx2) assign(coupfti_3214,coupfti) assign(pax6_4797,pax6) assign(fgf8_1104,fgf8) edge(1,2) edge(7,8) edge(9,10) edge(3,4) edge(5,6) edge(11,12) edge(3,14) edge(13,10) edge(6,11) edge(4,18) edge(8,15) edge(14,2) edge(16,10) edge(17,13) edge(15,13) edge(12,1) edge(2,20) edge(19,9) edge(18,16) edge(21,15) edge(15,21) edge(16,18)" - -# ╔═╡ 2457d4f1-a93f-407d-9e53-4e44f721568b -edges = res_sat |> split |> filter(startswith("edge")) .|> (x -> x[5:end]) .|> x -> replace(x, r"[()]" => "") .|> (x -> split(x, ",")) .|> (((x, y),) -> Pair(parse(Int, x), parse(Int, y))) - -# ╔═╡ d50e737f-4bb2-40a7-a906-663a2d296cc1 -graph = SimpleDiGraphFromIterator(Edge.(edges)) - -# ╔═╡ 113a60e4-8ef5-4d37-a978-d4ac9299d649 -plot(graph; markersize=0.25, fontsize=6, names=1:length(graph), curves=true, - method=:spring, - # nodesize=1, - self_edge_size=0.2 -) - -# ╔═╡ 4d863e2c-e9a5-4c32-8776-5427744ff411 -graph.fadjlist - -# ╔═╡ 3eff6e0d-79d2-4974-afaf-cfffbc620bf4 -md""" -### Setup -""" - -# ╔═╡ 8d6fe0bd-b670-47a6-91c6-0f1db32584ad -PlutoUI.TableOfContents() - -# ╔═╡ c3c26128-0e7e-4475-a057-591f74aec047 -df = collect_results(datadir("exp_raw", "biodivine_search")); - -# ╔═╡ c081cd85-b5af-4208-9ca8-90e2b1c02d0f -vertex_names_str = df.vertex_names |> unique |> only .|> x -> x[3:end] - -# ╔═╡ 4f017c2b-7f3d-4ff8-95ba-6581c55ee5f3 -vertex_names = replace.(lowercase.(vertex_names_str), "_" => "") - -# ╔═╡ ed8ac9f3-e361-4e05-a160-bbb5e3c2b2a1 -entity_string = "entity($(join(vertex_names, ";")))." - -# ╔═╡ 0fe52af5-b1d2-4b7b-aab1-8bb34044a8e5 -entity_string - -# ╔═╡ 3bff2b94-eef4-4185-be8f-1bca5774ff13 -@assert all(vertex_names |> Iterators.flatten |> collect |> filter(isletter) .|> islowercase) - -# ╔═╡ 61442d48-1005-4caa-af78-138dfcecc064 -@assert all(vertex_names |> Iterators.flatten |> collect |> filter(!isletter) .|> isdigit) - -# ╔═╡ a1a95edc-2a28-48a1-84cf-6b1ed635ae34 -@assert all(vertex_names .|> (x -> isletter(x[1]))) - -# ╔═╡ e1421a5b-11c7-44a8-b6f0-4654489a2b81 -states = df.selected_trajectories |> Iterators.flatten |> Iterators.flatten |> unique - -# ╔═╡ 8a2c00df-b9a9-42a2-ac0c-e243527545af -ids = eachindex(states) - -# ╔═╡ 1c9ab992-a1d4-4a03-af70-358389149f15 -state_string = "state(1..$(ids.stop))." - -# ╔═╡ 5f6aa260-a6d7-4605-9a58-585b403f52e0 -state_string - -# ╔═╡ 79c72d39-c2af-4b5e-8370-49f8e652a733 -@assert all(x -> x in states, steady_states) - -# ╔═╡ fdd9a8d5-7bc0-4121-94d9-f6259e44e240 -steady_ids = findfirst.(.==(steady_states), (states,)) - -# ╔═╡ 17cad63a-2003-4500-acef-a02c5567a428 -steady_state_string = "steady($(join(steady_ids, ";")))." - -# ╔═╡ dc058b46-0fa2-4197-a151-11ef46c892a7 -steady_state_string - -# ╔═╡ 4de0dced-294b-46c8-bb8a-c36922bf9df1 -function raw_connection_builder(solution_name, successes, specifications) - @assert length(successes) == length(specifications) - raw_connections = Tuple{Int, Int}[] - for (success, spec) in zip(successes, specifications) - id1 = findfirst(==(spec[1]), states) - id2 = findfirst(==(spec[2]), states) - if success[1] - push!(raw_connections, (id1, id2)) - end - - if success[2] - push!(raw_connections, (id2, id1)) - end - end - - return raw_connections -end - -# ╔═╡ 15ac2c7c-c01c-4c3c-a145-84bbdfb788ca -function connection_builder(solution_name, successes, specifications) - @assert length(successes) == length(specifications) - connection_strings = String[] - connects = (x, a, b) -> "connects($x, $a, $b)." - for (success, spec) in zip(successes, specifications) - id1 = findfirst(==(spec[1]), states) - id2 = findfirst(==(spec[2]), states) - if success[1] - push!(connection_strings, connects(solution_name, id1, id2)) - end - - if success[2] - push!(connection_strings, connects(solution_name, id2, id1)) - end - end - - return connection_strings -end - -# ╔═╡ 5840a8e6-1451-4637-9099-2256246baeb3 -@assert length(states) > 0 - -# ╔═╡ 070c8187-736d-4fe6-adbd-923e38d4f731 -states - -# ╔═╡ 56fb75e5-f3ae-47af-a425-a92c784087a6 -zip(vertex_names, eachcol((hcat(states...)'))) - -# ╔═╡ ac6532ec-3bd6-46e0-9ca9-8b3f18006fd3 -permutedims(DataFrame(zip(vertex_names, eachcol((hcat(states...)')))), 1) - -# ╔═╡ b917a2ab-f41a-45ff-83a4-60cb43c9feb3 -sol_ids = [df.exprs_and_scores[i] .|> (x -> x[4]) for i in eachindex(df.exprs_and_scores)] - -# ╔═╡ e495d254-df86-4ff4-9c3c-cfcbf8c8781e -length.(sol_ids) - -# ╔═╡ 691e4ff4-177a-4a12-b353-6cfc2b4180be -solution_names_stacked = [["$(vertex_names[vert])_$id" for id in sol_ids[vert]] for vert in eachindex(vertex_names)] - -# ╔═╡ 1996288a-5696-4d59-ab5f-f8f64dae1954 -solution_names = solution_names_stacked |> Iterators.flatten |> collect - -# ╔═╡ 356f324b-43bc-4f55-8c59-1582e35daf57 -solution_strings = ["solution($x)." for x in solution_names] - -# ╔═╡ 129079df-3878-4534-bd53-309dc3dea2bf -md""" -### Solution Pruning - -While we have $(length(solution_strings)) solutions, many of these solutions induce the same topology within the state space. If we only take the first (simplest) solution for each induced topology, we create much fewer constants for Clingo to work with, speeding up the grounding and possibly also the solving process. The per-transition successes show the topology each solution induces in the state space, so we can filter on unique values there. -""" - -# ╔═╡ 20ea6a2d-bf12-4233-b142-14fcd6771ef3 -@assert all(split.(solution_names, '_') .|> first |> unique .|> (x -> x in vertex_names)) - -# ╔═╡ 2235412f-48b1-4e85-915d-d2d37b2feabc -@assert all(split.(solution_names, '_') .|> last |> unique |> Iterators.flatten .|> isdigit) - -# ╔═╡ 8f09510b-c354-4061-bbb4-c5c69a7e17fb -@assert all(in.('_', solution_names)) - -# ╔═╡ 52d6f600-0c64-40f3-a171-dab105bcb9fc -@assert all(length.(split.(solution_names, '_')) .== 2) - -# ╔═╡ b8ae91eb-be7e-4794-9fdf-4379a7c6b9df -solution_names_stacked - -# ╔═╡ b45ae1e4-00f7-4648-9048-432f52d1ab3b -sol_exprs_printable = [df.exprs_and_scores[i] .|> (x -> x[1]) for i in eachindex(df.exprs_and_scores)][1][999] |> string |> x -> replace(x, "SoleLogics.Atom{SubString{String}}: v_" => "") - -# ╔═╡ 1c555d75-fa37-493f-9a6b-14bba03924da -pertransition_successes = [df.exprs_and_scores[i] .|> (x -> x[3]) for i in eachindex(df.exprs_and_scores)] - -# ╔═╡ f561ee12-0bb4-476e-9873-9b0504282321 -n_successes = length.(pertransition_successes) - -# ╔═╡ 963c1b25-d58a-4823-aa58-5c68e5c3bb8a -md""" -Unfiltered, we see that we have $(join(n_successes, ", ", " and ")) solutions for each of the 5 nodes in the model. -""" - -# ╔═╡ e79fe49c-1307-46ce-ac8e-f8a785f40e6a -unique_successes = pertransition_successes .|> unique - -# ╔═╡ c379dbbe-5cdc-494e-9228-81be16c3a2d1 -n_unique_successes = length.(unique_successes) - -# ╔═╡ 191f4126-95a6-4686-a898-0f28b2a67352 -sum(n_unique_successes) - -# ╔═╡ 7052b305-fc5f-4277-9ef2-b27f817350c4 -md""" -However, after filtering out unique values, we end up with only $(join(n_unique_successes, ", ", " and ")) for each! -""" - -# ╔═╡ afcd3d60-5be0-46dd-ba09-ae2fc7089059 -unique_indices = [unique(i -> x[i], eachindex(x)) for x in pertransition_successes] - -# ╔═╡ 459a1a38-f0d2-41b2-9e10-38ed10caa135 -solution_names_filtered_and_stacked = filtered ? [solutions[idxs] for (solutions, idxs) in zip(solution_names_stacked, unique_indices)] : solution_names_stacked - -# ╔═╡ 29c3b786-82bd-40e7-bb1d-0aa1f3e7921d -solution_names_filtered = solution_names_filtered_and_stacked |> Iterators.flatten |> collect - -# ╔═╡ 80d449f8-13ea-40d4-b3bd-58ca766a15ae -solution_strings_filtered = ["solution($x)." for x in solution_names_filtered] - -# ╔═╡ 36d8a531-a6c2-49eb-95ed-741c16b76209 -solution_strings_filtered - -# ╔═╡ 065f36d0-ea7e-4eb7-a82c-5236c3df0044 -belongs_to_strings = ["belongs_to($(entity)_$id, $entity)." for (entity, id) in split.(solution_names_filtered, "_")] - -# ╔═╡ e6f5276e-5660-435a-a151-8af927b0c301 -belongs_to_strings - -# ╔═╡ feb6c350-5eb9-462d-8ba9-6185fe97441c -@assert all(in.(assignments, (solution_names_filtered,))) - -# ╔═╡ c56a5fa7-18e9-4257-9bea-b85e51faeaca -sol_idxs = [only(findall(==(assignments[i]), solution_names_filtered_and_stacked[i])) for i in eachindex(assignments)] - -# ╔═╡ 52f6ab50-9c4f-41b4-b9f7-8a47cdad6c42 -@assert filtered ? (solution_names_filtered |> length) == (pertransition_successes |> Iterators.flatten |> unique |> length) : true - -# ╔═╡ 982b0fab-1eee-4231-a656-557484fb0aaf -pertransition_successes |> Iterators.flatten |> Iterators.flatten |> Iterators.flatten |> count - -# ╔═╡ 08f84a71-db13-47ff-8144-d6879fd7cb3c -pertransition_successes .|> unique |> Iterators.flatten |> collect - -# ╔═╡ 28af4ee4-0fb6-4377-b17d-ed1e8d1d3598 -pertransition_successes |> Iterators.flatten |> collect - -# ╔═╡ 97c803c6-00c0-42e0-9c97-ff015a51335c -pertransition_successes |> Iterators.flatten .|> unique |> collect - -# ╔═╡ 8b570775-6afe-435d-b3f6-4030b0df5e6c -sol_trans = [pertransition_successes[i][sol_idxs[i]] for i in eachindex(sol_idxs)] - -# ╔═╡ 56e58ef6-e48f-4971-ba3b-e88a0957b4c7 -first_transition in (pertransition_successes |> Iterators.flatten |> unique) - -# ╔═╡ 49112c99-533c-4f96-acfe-1f660df01d74 -df.selected_trajectories .|> first - -# ╔═╡ 2ba7e499-44e0-435d-8f2f-dba92a7270fe -foreach(x -> println(x), df.selected_trajectories[1]) - -# ╔═╡ 9fe9fae0-ef94-4b40-89ea-32eb314013d0 -sol_connections = [df.exprs_and_scores[i] .|> (x -> x[3]) for i in eachindex(df.exprs_and_scores)] - -# ╔═╡ 414b38e6-c0c1-4429-907e-a7f836f5312c -trajectories = [df.selected_trajectories[i] .|> collect for i in eachindex(df.selected_trajectories)] - -# ╔═╡ 072404b8-a243-4b40-86ea-52393754de4f -raw_connections_stacked = [[raw_connection_builder(name[i], conn[i], traj) for i in eachindex(name)] for (name, conn, traj) in zip(solution_names_filtered_and_stacked, sol_connections, trajectories)] - -# ╔═╡ 4a4190e7-6c01-47b6-801c-97b3f3165050 -raw_connections_stacked |> Iterators.flatten |> Iterators.flatten |> collect |> length - -# ╔═╡ 75257860-f733-441f-95ee-93c6d1b7ae2a -connection_strings_stacked = [[connection_builder(name[i], conn[i], traj) for i in eachindex(name)] for (name, conn, traj) in zip(solution_names_filtered_and_stacked, sol_connections, trajectories)] - -# ╔═╡ 56448558-2952-4764-8766-36e8c62d1de5 -connection_strings = connection_strings_stacked |> Iterators.flatten |> Iterators.flatten |> collect - -# ╔═╡ caf5fbec-f323-45ed-94c9-bfd0957ff97c -connection_strings - -# ╔═╡ b563aa33-ee26-4c12-98f6-65d02cd3899d -mktemp() do path, io - println(io, entity_string) - println(io, state_string) - for x in solution_strings_filtered - println(io, x) - end - println(io, steady_state_string) - for x in belongs_to_strings - println(io, x) - end - for x in connection_strings - println(io, x) - end - println(io, model_def) - close(io) - # @show length(readlines(path)) - - # @benchmark - run(pipeline(ignorestatus(`$(Clingo_jll.clingo()) -n10000 $path`))) - # setup=(path=$path) -end - -# ╔═╡ 25dd6bf1-4ca2-4084-b682-858020d90bbc -sol_exprs = [df.exprs_and_scores[i] .|> (x -> x[1]) for i in eachindex(df.exprs_and_scores)] - -# ╔═╡ d5b485db-6b4d-4ede-ae64-bf8448451ddf -selected_exprs = [sol_exprs[i][sol_idxs[i]] for i in eachindex(sol_idxs)] - -# ╔═╡ Cell order: -# ╟─f09edd39-ffeb-4850-b29e-b38565273ff5 -# ╟─c081cd85-b5af-4208-9ca8-90e2b1c02d0f -# ╠═4f017c2b-7f3d-4ff8-95ba-6581c55ee5f3 -# ╠═ed8ac9f3-e361-4e05-a160-bbb5e3c2b2a1 -# ╟─701cca9d-f8de-4160-8f5d-c1ce30d31525 -# ╟─e1421a5b-11c7-44a8-b6f0-4654489a2b81 -# ╟─8a2c00df-b9a9-42a2-ac0c-e243527545af -# ╠═1c9ab992-a1d4-4a03-af70-358389149f15 -# ╟─690734ed-bdbf-4635-a4d2-37500fcea3cf -# ╠═b917a2ab-f41a-45ff-83a4-60cb43c9feb3 -# ╠═b45ae1e4-00f7-4648-9048-432f52d1ab3b -# ╠═e495d254-df86-4ff4-9c3c-cfcbf8c8781e -# ╠═691e4ff4-177a-4a12-b353-6cfc2b4180be -# ╠═1996288a-5696-4d59-ab5f-f8f64dae1954 -# ╠═356f324b-43bc-4f55-8c59-1582e35daf57 -# ╟─129079df-3878-4534-bd53-309dc3dea2bf -# ╠═b8ae91eb-be7e-4794-9fdf-4379a7c6b9df -# ╟─1c555d75-fa37-493f-9a6b-14bba03924da -# ╟─f561ee12-0bb4-476e-9873-9b0504282321 -# ╟─963c1b25-d58a-4823-aa58-5c68e5c3bb8a -# ╠═e79fe49c-1307-46ce-ac8e-f8a785f40e6a -# ╟─c379dbbe-5cdc-494e-9228-81be16c3a2d1 -# ╠═191f4126-95a6-4686-a898-0f28b2a67352 -# 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b/experiments/Synth/notebooks/march_2025_presentation.jl deleted file mode 100644 index a2ff728..0000000 --- a/experiments/Synth/notebooks/march_2025_presentation.jl +++ /dev/null @@ -1,778 +0,0 @@ -### A Pluto.jl notebook ### -# v0.20.4 - -using Markdown -using InteractiveUtils - -# ╔═╡ b6578081-a6fe-4637-903e-4435f7e6c749 -using DrWatson - -# ╔═╡ d03d06ee-5614-4fdd-889d-48c8710b261c -begin - quickactivate(@__DIR__) - using Revise - using GraphDynamicalSystems - using Synth - using Herb - using MetaGraphsNext: labels - import MetaGraphsNext - using SoleLogics: value, Atom - # using ProgressMeter, DataFrames, HerbSearch, Random - using MetaGraphsNext: labels - # using Statistics: quantile - # using DynamicalSystems - using Clingo_jll - using Plots - using Graphs - using GraphRecipes - using PlutoUI - # using Herb - using SoleLogics - using MLStyle - import HerbConstraints.pattern_match - using HerbConstraints: PatternMatchResult, PatternMatchSuccess, PatternMatchSoftFail, PatternMatchHardFail, PatternMatchSuccessWhenHoleAssignedTo -end - -# ╔═╡ fa93f89e-05ab-11f0-2dfe-f1e019d9bfe8 -html""" -

-
-

-Synthesizing -Networks of State Machines -

-

-Synthesizing -Dynamical Systems -

- -

Synthesizing Biological Models

-

-Synthesizing -Qualitative Networks -

-

-Synthesizing -Boolean Networks -

-
- -
03/26/2024 Update Meeting
- -""" - -# ╔═╡ a1718e12-bfdf-44fe-a930-c9131e64d144 -md""" -## Agenda - -1. Unified formulation of the synthesis problem -2. What we have -3. What we're missing -4. Questions -""" - -# ╔═╡ 3e8e48cc-790c-4a18-990a-637372d432d2 -md""" -## Synthesis Problem Formulation - -Given: - -- A *grammar* for target functions (arithmetic for QNs, logical operators for BNs) - - Includes a set of *genes/proteins* -- A *specification*, which is one or both of - - *Longitudinal data*: state transition pairs and steady states - - *Perturbation data*: fixed values for a subset of genes/proteins, and steady states - - -Find: - -- A set of *target functions* for each gene/protein -- Satisfying combinations of target functions must match the specification's - - Steady states (under perturbation as well, if given) - - State transition pairs, if given -""" - -# ╔═╡ 1b53d4e4-fb36-4c8a-9e35-55c3832f0005 -md""" -### Formulation Examples - -!!! tip "Target Functions" - Set of functions, one per gene/protein - ```julia - Coup_fti = ... - Emx2 = Coup_fti / (1 - Fgf8) - Fgf8 = ... - ``` - When assembled into a complete model (QN, for example), a satisfying set of functions should match the specification (longitudinal and/or perturbation) -""" - -# ╔═╡ ef9ea3aa-1022-409e-8b6e-d79e0455bfc8 -md""" -## ✓ What we have - -A synthesis pipeline that starts either from - -- An existing model, sampling longitudinal data, and steady state(s) from it -- Longitudinal data and steady state(s) directly - -and synthesizes a set of target functions whose combination matches the longitundal data and steady states. - -The pipeline: - -1. Constructs a grammar for the problem -2. Enumerates candidate functions from the grammar -3. Checks candidate functions against the longitudinal data (state transition pairs) -4. Uses the result to select possible combinations of target functions that have the specified steady state(s) -""" - -# ╔═╡ f00faa20-d261-433d-908e-8047b5a5d45a -md""" -### Walk through - -1. The model we start with -2. Sampling data from it -3. Synthesizing possible target functions -4. Combining to a complete model -""" - -# ╔═╡ df82d6bc-2c8b-48ec-b4fc-7d0c0ce9d7d3 -md""" -#### Original Model -""" - -# ╔═╡ f38f30d0-c875-4f1a-819d-a4b20cb382ff -md""" -!!! info "" - Giacomantonio CE, Goodhill GJ (2010) A Boolean Model of the Gene Regulatory Network Underlying Mammalian Cortical Area Development. PLoS Comput Biol 6(9): e1000936. [https://doi.org/10.1371/journal.pcbi.1000936](https://doi.org/10.1371/journal.pcbi.1000936) -""" - -# ╔═╡ a03aab2a-8af1-4c0c-aae7-ef34b96c1702 -begin - n_trajectories = 10 - length_trajectory = 10 - max_iterations = 50000 - max_depth = 3 -end - -# ╔═╡ 4710d9e8-7781-4b01-bec5-c8e30219b5c2 -s1 = [0, 0, 1, 1, 1] - -# ╔═╡ 648ddc71-f6f3-44ac-b44a-8d0f1a5ed958 -s2 = [1, 0, 1, 1, 1] - -# ╔═╡ fd621392-67d6-4b3d-beb7-04c963f5b993 -searching = Set([s1, s2]) - -# ╔═╡ 9dc68992-7f48-4ed7-af1e-61bc6d693f8c -md""" -### Combining Local Solutions - -→ Other notebook -""" - -# ╔═╡ d4604287-fde2-4fc9-961b-4505e8210551 -md""" -## ❗ What we're missing - -- Current models will definitely enter all steady states, but might enter other steady states outside of the specification - - Next step is to verify this -""" - -# ╔═╡ 426f51b8-c9ec-42e9-8d21-9573183f48f4 -md""" -## ❓ Questions - -- When *you* use BMA, what do you verify? - - If something is wrong, how do you use the outcome of the verification to fix it? -- What does model stabilization mean to you? - - Do oscillating states count? -- Can we assume we have all steady states present in the data? -- When working with perturbation data, what can we start from as a target function? -- Any patterns of target functions that you often see? -- Current approach can create “correct” models that visit states that were not in the data - - Do we want to minimize this? - - For example, which one to choose? - - We have a simple model that visits many states not in the data, but has the correct dynamics - - We have a complex model that stays within the states in the data -- Which specific papers to re-do? - -""" - -# ╔═╡ cdc9908c-d858-4cee-ac08-2b135e8aabd2 -md""" -# Appendix -""" - -# ╔═╡ f9e3c41e-1ab9-4a58-aaad-5c97c888728c -md""" -## Data Loading -""" - -# ╔═╡ 48bf71ae-ca36-40aa-a65f-e7eb37953058 -begin - model_df = collect_results(datadir("src_parsed", "biodivine_benchmark_as_metagraphs");); - path2id = path -> parse(Int, splitext(basename(path))[1]) - model_df.ID = path2id.(model_df.path) - model007 = only(model_df[model_df.ID .== 7, :metagraph_model]) -end - -# ╔═╡ 86faab19-6712-4d7e-aeb8-1ea1febc3b97 -original_model_plot = plot(reverse(model007.graph); names=labels(model007) .|> string .|> (x -> x[39:end]), method=:shell, self_edge_size=0.12, nodecolor=:white, nodeshape=:circle) - -# ╔═╡ 9551231b-753d-4958-8444-23cbdae1146e -savefig(original_model_plot, "original_model_plot.png") - -# ╔═╡ fe8fbd0b-c384-4d93-ab5a-230d67ec6f8f -functions007 = Pair.(labels(model007) .|> string .|> (x -> x[39:end]), (labels(model007) .|> (x -> model007[x]) .|> string .|> (x -> x[26:end]) .|> (x -> replace(x, "v_" => "")))) |> Dict; - -# ╔═╡ ad6776d9-6fec-467a-a75e-c0ec86afdaf9 -md""" -$(Markdown.Table([["Gene", "Target Function"], [[k, v] for (k, v) in functions007]...], [:l, :l])) -""" - -# ╔═╡ 94d4c053-c02e-4a07-93b2-63438759acb8 -begin - trajectories = [] - for i in 1:n_trajectories - abn7 = BooleanNetworks.abn(model007; seed=i) - data = gather_bn_data(abn7, length_trajectory) - push!(trajectories, data) - end - states = trajectories |> Iterators.flatten |> unique -end - -# ╔═╡ 2abbdd40-8e4e-49d6-ad5a-ecfa46c588d8 -for s in states - print("[], ") - for x in s - print("[$x], ") - end - println() -end - -# ╔═╡ 754c499a-28e3-42fb-bed3-e466ed1c7393 -begin - io_pairs = [] - for (i, s) in enumerate(states) - adjacents = states .- (s,) .|> (x -> abs.(x)) .|> sum .|> ==(1) - - for other_state in states[adjacents] - push!(io_pairs, (s, other_state)) - end - end - io_pairs -end - -# ╔═╡ b21cb05c-f72e-411f-a2fd-b675e84e23b7 -md""" -### Formulation Examples - -$(Markdown.Admonition("tip", "Specification: Longitudinal Data", [ - Markdown.Paragraph("State transition pairs such as"), - Markdown.Code("julia", join(string.(io_pairs[1:3]), "\n") * "\n..."), - Markdown.Paragraph("With one or more of these states marked as steady"), -])) - -!!! warning "Specification: Perturbation Data" - Fixed value, and a corresponding steady state - - ```julia - [□, □, □, 1, □] → [0, 0, 1, 1, 0] - ``` - - `□`: unfixed value - -""" - -# ╔═╡ 282c25ec-8847-4ca9-bee9-7b9d0874c2b8 -begin - state_space = MetaGraphsNext.MetaGraph(SimpleGraph(), label_type=AbstractVector) - add_vertex!.((state_space,), states) - [add_edge!(state_space, x[1], x[2]) for x in io_pairs] -end - -# ╔═╡ 7a67e9fe-7bf9-4117-8e00-10afcee2f239 -state_space_plot = plot(state_space.graph, curves=false, method=:stress) - -# ╔═╡ f2c73222-3c36-415a-97ae-b8a1a15b574f -savefig(state_space_plot, "state_space.png") - -# ╔═╡ 91f44e97-bbac-413d-8331-dc1328f96f89 -md""" -### Converting to IO Pairs - -Then, we convert to input/output pairs to check the target functions. In this case, we are assuming an asynchronous update schedule, so every pair of states that is a hamming distance of 1 apart becomes an example. - -$(Markdown.Code("julia", join(string.(io_pairs[1:3]), "\n") * "\n...")) - -""" - -# ╔═╡ cd238a3a-d6a3-464b-a249-d2e66236ee50 -unique_transitions = io_pairs .|> Set |> unique .|> collect - -# ╔═╡ e4732348-0188-4fb0-a0cd-e1888ec5f9fd -state_string = "state(1..$(length(states)))." - -# ╔═╡ 911de256-b75e-4503-9702-c70e70be5779 -entity_names = model007 |> labels .|> value .|> (x -> replace(x, "v_" => "")) .|> Symbol - -# ╔═╡ d42cbcbd-5990-4818-9edb-02d631486667 -md""" -### Sampling Longitudinal Data - -Data comes as a set of states. Each row here is a state. - -$(Markdown.Table([["Gene", string.(entity_names)...], [vcat("", s) for (i, s) in enumerate(states)]...], [:l, :l, :l, :l, :l, :l])) - -""" - -# ╔═╡ 2cf1d0f3-5f6f-4fd7-ac97-9072f25f9917 -problems = [ - UndirectedProblem( - string(entity_names[i]), - [ - UndirectedExample( - Dict(:state => t[1]), - Dict(:state => t[2]) - ) for t in unique_transitions if t[1][i] != t[2][i] - ] - ) for i in 1:length(entity_names) -] - -# ╔═╡ 8e075539-3c74-4506-9191-74c0c2944d94 -[Set([x.data1[:state], x.data2[:state]]) for x in problems[1].examples] - -# ╔═╡ 5ccb2ec7-b0ee-48de-9acc-a44de5ddb57d -problems[1].examples - -# ╔═╡ 9fece869-b636-410e-a480-9f5fcbec5a75 -entity_string = "entity($(join(entity_names, ";")))." - -# ╔═╡ a6657d7d-0bc3-4c83-bbef-c43e519d6cb4 -base_grammar_string = let io = IOBuffer() - Base.show(io, GraphDynamicalSystems.base_qn_grammar) - String(take!(io)) -end - -# ╔═╡ 9925d04e-e042-4d14-ad37-bba85aa54a34 -md""" -### Formulation Examples - -$(Markdown.Admonition("info", "Grammar", [ - Markdown.Paragraph("QN grammar, matching the BMA tool"), - Markdown.Code("julia", base_grammar_string), - Markdown.Paragraph("In a full grammar, we also have `Val = Gene/Protein` for each one in the problem"), -])) -""" - -# ╔═╡ e6fdb952-424a-492f-b9a4-01984a4824bb -function build_qn_grammar( - entity_names, - constants = default_qn_constants, -) - g = deepcopy(GraphDynamicalSystems.base_qn_grammar) - n_original_rules = length(g.rules) - n_entities = length(entity_names) - - for e in entity_names - add_rule!(g, :(Val = $e)) - end - - for c in constants - add_rule!(g, :(Val = $c)) - end - - original_rules_domain = BitVector(zeros(length(g.rules))) - original_rules_domain[[1:n_original_rules...]] .= true - entity_domain = BitVector(zeros(length(g.rules))) - entity_domain[[n_original_rules+1:n_original_rules + n_entities...]] .= true - constants_domain = .~(entity_domain .| original_rules_domain) - - # +, *, min, max, are all commutative - domain = BitVector(zeros(length(g.rules))) - @. domain[[1, 4:6...]] = true - template_tree = DomainRuleNode(domain, [VarNode(:a), VarNode(:b)]) - order = [:a, :b] - - addconstraint!(g, Ordered(template_tree, order)) - - # Forbid same arguments for 2-argument functions - domain = BitVector(zeros(length(g.rules))) - @. domain[length(g.childtypes) == 2] = true - template_tree = DomainRuleNode(domain, [VarNode(:a), VarNode(:a)]) - - addconstraint!(g, Forbidden(template_tree)) - - # Forbid Ceil and Floor from including an entity or constant directly - domain = BitVector(zeros(length(g.rules))) - domain[[n_original_rules+1:length(g.rules)...]] .= true - - entities_consts = DomainRuleNode(domain) - - domain = BitVector(zeros(length(g.rules))) - domain[[7, 8]] .= true - - template_tree = DomainRuleNode(domain, [entities_consts]) - - addconstraint!(g, Forbidden(template_tree)) - - # Forbid ceil(floor(x)) and vice-versa - ceil_or_floor = BitVector(zeros(length(g.rules))) - ceil_or_floor[[7, 8]] .= true - template_tree = - DomainRuleNode(ceil_or_floor, [DomainRuleNode(ceil_or_floor, [VarNode(:a)])]) - - addconstraint!(g, Forbidden(template_tree)) - - # Forbid constants as numerator of / - addconstraint!(g, Forbidden(RuleNode(3, [ - DomainRuleNode(constants_domain), - VarNode(:a) - ]))) - - # Force programs to reference at least one entity - addconstraint!(g, ContainsSubtree(DomainRuleNode(entity_domain))) - - # Forbid programs to add or subtract 0 - # zero_index = only(findall(g.rules .== (:(0),))) - # add_subtract_domain = (g.rules .== (:(Val + Val),)) .| (g.rules .== (:(Val - Val),)) - # addconstraint!(g, Forbidden(DomainRuleNode(add_subtract_domain, [VarNode(:a), RuleNode(zero_index)]))) - - # Forbid programs to multiply by 0 or 1 - zero_one_domain = (g.rules .== (:(0),)) .| (g.rules .== (:(1),)) - multiply_index = only(findall((g.rules .== (:(Val * Val),)))) - addconstraint!(g, Forbidden(RuleNode(multiply_index, [VarNode(:a), DomainRuleNode(zero_one_domain)]))) - - # Forbid division by 0 or 1 - zero_domain = (g.rules .== (:(0),)) .| (g.rules .== (:(1),)) - division_index = only(findall((g.rules .== (:(Val / Val),)))) - addconstraint!(g, Forbidden(RuleNode(division_index, [VarNode(:a), DomainRuleNode(zero_domain)]))) - - # Forbid min and max - # min_max_domain = (g.rules .== (:(Min(Val, Val)),)) .| (g.rules .== (:(Max(Val, Val)),)) - - # addconstraint!(g, Forbidden(DomainRuleNode(min_max_domain, [VarNode(:a), VarNode(:b)]))) - - # Forbid both constants for 2-argument functions - domain = BitVector(zeros(length(g.rules))) - @. domain[length(g.childtypes) == 2] = true - template_tree = DomainRuleNode(domain, [ - DomainRuleNode(constants_domain), - DomainRuleNode(constants_domain) - ]) - - addconstraint!(g, Forbidden(template_tree)) - - # * by 1 - - return g -end - -# ╔═╡ 5aebfdef-39d4-431e-826b-3039c95af013 -grammar = build_qn_grammar( - entity_names, - [1] -) - -# ╔═╡ 21ebebde-1794-47e2-a3cb-d1b78bff2c6e -function make_iter() - return Herb.BFSIterator(grammar, :Val; max_depth) -end - -# ╔═╡ fcb30d9c-ca63-4a9d-8b64-a178fdba50a7 -total_number_progs_possible = begin - iter = make_iter() - length(iter) -end - -# ╔═╡ f8e7784b-e706-4ac8-8373-f60566a30ddc -grammar_string = let io = IOBuffer() - Base.show(io, grammar) - String(take!(io)) -end - -# ╔═╡ cb24a482-5323-4a3d-b03b-6a8d4b9a6183 -md""" -### Enumerating Candidate Functions - -From our grammar - -$(Markdown.Code("julia", grammar_string)) - -We enumerate all programs up to a certain depth (which is $total_number_progs_possible in total, in this case) - -We apply a number of constraints in the enumeration process to prune many possible functions. - -- +, *, min, max, are all commutative -- Forbid same arguments for 2-argument functions -- Forbid division by 1 -- Forbid only constants for 2-argument functions -- Forbid `ceil` and `floor` from including a gene or constant directly -- Forbid `ceil(floor(x))` and vice-versa -- Forbid constants as numerator of / - -""" - -# ╔═╡ e94f29d0-ea06-4678-86b4-3481db701c9d -function Synth.synth_biodivine( - problem, - iterator, - grammar, - max_iterations, - evaluator, - vertex_names, -) - exprs_and_scores = [] - - for (i, ex) in enumerate(iterator) - if i % 100000 == 0 - @info "$i iterations, problem $(problem.name)" - end - - expr = rulenode2expr(ex, grammar) - - sat_examples = nothing - try - sat_examples = evaluator(problem, expr, vertex_names) - catch e - @error "Problem evaluating: Problem name $(problem.name), expr: $expr, i: $i." - rethrow(e) - end - - if isnothing(sat_examples) - push!(exprs_and_scores, (expr, nothing, nothing, i)) - # if all examples worked in at least one direction - elseif sum(all.(==(false), sat_examples)) == 0 - score = sum(count.(sat_examples)) / (2 * length(problem.examples)) - push!(exprs_and_scores, (expr, score, sat_examples, i)) - end - - if i > max_iterations - @warn "Maximum iterations reached" - break - end - end - - return exprs_and_scores -end - -# ╔═╡ 938bcef0-2e9d-43bf-9886-c012034453dc -function interpret_qn( - e,# ::Union{AbstractString,Integer,Expr,Atom}, - qn_state, #::AbstractVector{<:Integer}, - vertex_names #::AbstractVector{<:AbstractString}, -) - state_map = Dict(zip(vertex_names, deepcopy(qn_state))) - - _int(e) = @match e begin - ::AbstractString => state_map[e] - ::Atom => state_map[e] - ::Integer => e - ::Symbol => state_map[string(e)] - :($v1 + $v2) => _int(v1) + _int(v2) - :($v1 - $v2) => _int(v1) - _int(v2) - :($v1 / $v2) => _int(v1) / _int(v2) - :($v1 * $v2) => _int(v1) * _int(v2) - :(Min($v1, $v2)) => min(_int(v1), _int(v2)) - :(Max($v1, $v2)) => max(_int(v1), _int(v2)) - :(Ceil($v)) => ceil(_int(v)) - :(Floor($v)) => floor(_int(v)) - _ => error("Unhandled Expr in `interpret`: $e, $(typeof(e))") - end - - return _int(e) -end - -# ╔═╡ 62e4af8c-b1cc-428b-981c-f5600f65b714 -function Synth.evaluate_qn(problem::UndirectedProblem, expr, vertex_names) - sat_examples = BitVector[] - vertex_index = only(findall(==(problem.name), vertex_names)) - - function _eval_1_dir(in, out) - res = interpret_qn(expr, in[:state], vertex_names) - expected = out[:state][vertex_index] - success = expected == res - return success - end - - for example ∈ problem.examples - success_direction1 = _eval_1_dir(example.data1, example.data2) - success_direction2 = _eval_1_dir(example.data2, example.data1) - - success = BitVector([success_direction1, success_direction2]) - - push!(sat_examples, success) - end - - return sat_examples -end - -# ╔═╡ 3f2f2fe4-3c84-4bf0-ba7f-8168f5d840bf -results = synth_biodivine.( - problems, - (make_iter(),), - (grammar,), - (max_iterations,), - (evaluate_qn,), - (string.(entity_names),) -) - -# ╔═╡ ca878159-a262-46f4-a012-03b6f3ee3ae2 -[r for r in results[1] if r[3][1] == [false, true]][begin:begin+20] .|> (x -> x[1]) .|> string .|> (x -> println(x)) - -# ╔═╡ c1478d13-7168-4dc0-ab2a-5e4e792f910d -length(results[1]) - 20 - -# ╔═╡ 06002e18-f6b4-4e69-aabf-7b8537f03c3c -[r for r in results[1] if r[3][1] == [false, true]][begin:begin+20] .|> (x -> x[1]) .|> string .|> (x -> replace( - x, - "*" => "times", - "Min" => "\"Min\"", - "Max" => "\"Max\"", - (string.(entity_names) .=> ["\"$e\"" for e in entity_names])... -)) .|> (x -> println(x)) - -# ╔═╡ 1473d4f2-4665-43e9-91f7-5bd6e9f088cb -println.(map(x -> string(x[1]), results[1])[1:100]) - -# ╔═╡ 5bfbfb20-cfb3-40d4-b707-e737f527402a -length.(results) - -# ╔═╡ a32ee36a-535f-44ed-997f-7d887a4e2345 -n_programs_per_entity = ["$name → $num" for (name, num) in zip(entity_names, length.(results))] - -# ╔═╡ 64eeefec-9129-4f07-947f-73d611ee6c1c -md""" -### Summary Example - -- Unique states (sampled): $(length(states)) -- State transition pairs (Hamming distance 1): $(length(unique_transitions)) -- \# of functions possible (max-depth: 3): $total_number_progs_possible -- \# of functions that satisfy the transitions: $(join(n_programs_per_entity, ", ", ", and ")) -""" - -# ╔═╡ a31cfabd-8894-42f2-95d7-201ba06a853d -n_programs_foreach_entity = ["$num for $name" for (name, num) in zip(entity_names, length.(results))] - -# ╔═╡ f58351c9-f49d-4a0a-9137-06d8c68d6251 -md""" -### Check against IO Examples - -With candidate programs, and IO examples, we can check which candidate programs correctly transform input to output for each example. - -This reduces the number of candidate functions for each entity to $(join(n_programs_foreach_entity, ", ", ", and ")). - -This step also gives us the information for which *direction* each candidate function works in -""" - -# ╔═╡ edd72405-6889-4cbc-9b5f-4378130b0d31 -sol_ids = [results[i] .|> (x -> x[4]) for i in eachindex(results)] - -# ╔═╡ 1054aa3b-4675-4054-8eb9-1ff931dcbda6 -md""" -## Notebook Setup -""" - -# ╔═╡ 00383472-8b56-4504-9f9f-2b2a737e7fb4 -TableOfContents(title="26/03/25", depth=2) - -# ╔═╡ bd4c5a4d-e23d-49e6-82f4-3a21f9f27119 -html""" - -""" - -# ╔═╡ Cell order: -# ╟─fa93f89e-05ab-11f0-2dfe-f1e019d9bfe8 -# ╟─a1718e12-bfdf-44fe-a930-c9131e64d144 -# 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-belongs_to(emx2_5, emx2). -belongs_to(emx2_19, emx2). -belongs_to(emx2_28, emx2). -belongs_to(emx2_58, emx2). -belongs_to(emx2_1044, emx2). -belongs_to(emx2_1058, emx2). -belongs_to(emx2_1065, emx2). -belongs_to(emx2_1393, emx2). -belongs_to(emx2_1415, emx2). -belongs_to(emx2_1417, emx2). -belongs_to(emx2_1679, emx2). -belongs_to(emx2_1997, emx2). -belongs_to(emx2_2009, emx2). -belongs_to(emx2_6325, emx2). -belongs_to(emx2_6407, emx2). -belongs_to(emx2_6409, emx2). -belongs_to(emx2_6410, emx2). -belongs_to(coupfti_1, coupfti). -belongs_to(coupfti_3, coupfti). -belongs_to(coupfti_4, coupfti). -belongs_to(coupfti_6, coupfti). -belongs_to(coupfti_9, coupfti). -belongs_to(coupfti_12, coupfti). -belongs_to(coupfti_28, coupfti). -belongs_to(coupfti_44, coupfti). -belongs_to(coupfti_78, coupfti). -belongs_to(coupfti_97, coupfti). -belongs_to(coupfti_128, coupfti). -belongs_to(coupfti_132, coupfti). -belongs_to(coupfti_764, coupfti). -belongs_to(coupfti_1057, coupfti). -belongs_to(coupfti_1395, coupfti). -belongs_to(coupfti_1407, coupfti). -belongs_to(coupfti_1409, coupfti). -belongs_to(coupfti_1596, coupfti). -belongs_to(coupfti_1671, coupfti). -belongs_to(coupfti_1677, coupfti). -belongs_to(coupfti_2028, coupfti). -belongs_to(coupfti_2216, coupfti). -belongs_to(coupfti_2266, coupfti). -belongs_to(coupfti_2298, coupfti). -belongs_to(coupfti_3210, coupfti). -belongs_to(coupfti_3214, coupfti). -belongs_to(coupfti_3216, coupfti). -belongs_to(coupfti_3218, coupfti). -belongs_to(coupfti_3220, coupfti). -belongs_to(coupfti_5031, coupfti). -belongs_to(coupfti_6255, coupfti). -belongs_to(coupfti_6259, coupfti). -belongs_to(coupfti_6339, coupfti). -belongs_to(coupfti_6342, coupfti). -belongs_to(coupfti_6357, coupfti). -belongs_to(coupfti_6381, coupfti). -belongs_to(coupfti_6384, coupfti). -belongs_to(coupfti_6385, coupfti). -belongs_to(coupfti_6429, coupfti). -belongs_to(coupfti_6431, coupfti). -belongs_to(coupfti_6432, coupfti). -belongs_to(coupfti_6436, coupfti). -belongs_to(coupfti_6443, coupfti). -belongs_to(coupfti_6446, coupfti). -belongs_to(pax6_1, pax6). -belongs_to(pax6_3, pax6). -belongs_to(pax6_5, pax6). -belongs_to(pax6_6, pax6). -belongs_to(pax6_17, pax6). -belongs_to(pax6_21, pax6). -belongs_to(pax6_28, pax6). -belongs_to(pax6_35, pax6). -belongs_to(pax6_57, pax6). -belongs_to(pax6_136, pax6). -belongs_to(pax6_783, pax6). -belongs_to(pax6_958, pax6). -belongs_to(pax6_972, pax6). -belongs_to(pax6_1000, pax6). -belongs_to(pax6_1049, pax6). -belongs_to(pax6_1059, pax6). -belongs_to(pax6_1407, pax6). -belongs_to(pax6_1997, pax6). -belongs_to(pax6_2021, pax6). -belongs_to(pax6_2142, pax6). -belongs_to(pax6_2266, pax6). -belongs_to(pax6_6261, pax6). -belongs_to(pax6_6262, pax6). -belongs_to(pax6_6265, pax6). -belongs_to(pax6_6325, pax6). -belongs_to(pax6_6326, pax6). -belongs_to(pax6_6347, pax6). -belongs_to(pax6_6348, pax6). -belongs_to(pax6_6349, pax6). -belongs_to(pax6_6393, pax6). -belongs_to(pax6_6395, pax6). -belongs_to(pax6_6398, pax6). -belongs_to(fgf8_1, fgf8). -belongs_to(fgf8_4, fgf8). -belongs_to(fgf8_6, fgf8). -belongs_to(fgf8_20, fgf8). -belongs_to(fgf8_759, fgf8). -belongs_to(fgf8_1671, fgf8). -belongs_to(fgf8_2278, fgf8). -belongs_to(fgf8_6362, fgf8). -belongs_to(fgf8_6441, fgf8). -connects(sp8_3, 1, 2). -connects(sp8_3, 4, 3). -connects(sp8_3, 6, 5). -connects(sp8_3, 7, 8). -connects(sp8_3, 9, 10). -connects(sp8_4, 2, 1). -connects(sp8_4, 3, 4). -connects(sp8_4, 6, 5). -connects(sp8_4, 8, 7). -connects(sp8_4, 9, 10). -connects(sp8_5, 2, 1). -connects(sp8_5, 4, 3). -connects(sp8_5, 6, 5). -connects(sp8_5, 7, 8). -connects(sp8_5, 10, 9). -connects(sp8_24, 2, 1). -connects(sp8_24, 4, 3). -connects(sp8_24, 6, 5). -connects(sp8_24, 7, 8). -connects(sp8_24, 10, 9). -connects(sp8_78, 2, 1). -connects(sp8_78, 3, 4). -connects(sp8_78, 5, 6). -connects(sp8_78, 7, 8). -connects(sp8_78, 9, 10). -connects(sp8_96, 2, 1). -connects(sp8_96, 3, 4). -connects(sp8_96, 5, 6). -connects(sp8_96, 7, 8). -connects(sp8_96, 9, 10). -connects(sp8_99, 2, 1). -connects(sp8_99, 3, 4). -connects(sp8_99, 5, 6). -connects(sp8_99, 7, 8). -connects(sp8_99, 9, 10). -connects(sp8_128, 2, 1). -connects(sp8_128, 3, 4). -connects(sp8_128, 5, 6). -connects(sp8_128, 7, 8). -connects(sp8_128, 9, 10). -connects(sp8_129, 2, 1). -connects(sp8_129, 3, 4). -connects(sp8_129, 5, 6). -connects(sp8_129, 7, 8). -connects(sp8_129, 9, 10). -connects(sp8_131, 2, 1). -connects(sp8_131, 3, 4). -connects(sp8_131, 5, 6). -connects(sp8_131, 7, 8). -connects(sp8_131, 9, 10). -connects(sp8_1399, 2, 1). -connects(sp8_1399, 3, 4). -connects(sp8_1399, 5, 6). -connects(sp8_1399, 7, 8). -connects(sp8_1399, 9, 10). -connects(sp8_1407, 2, 1). -connects(sp8_1407, 3, 4). -connects(sp8_1407, 5, 6). -connects(sp8_1407, 7, 8). -connects(sp8_1407, 9, 10). -connects(sp8_1409, 1, 2). -connects(sp8_1409, 4, 3). -connects(sp8_1409, 6, 5). -connects(sp8_1409, 8, 7). -connects(sp8_1409, 10, 9). -connects(sp8_1661, 2, 1). -connects(sp8_1661, 3, 4). -connects(sp8_1661, 6, 5). -connects(sp8_1661, 7, 8). -connects(sp8_1661, 9, 10). -connects(sp8_1671, 2, 1). -connects(sp8_1671, 4, 3). -connects(sp8_1671, 6, 5). -connects(sp8_1671, 7, 8). -connects(sp8_1671, 9, 10). -connects(sp8_1995, 2, 1). -connects(sp8_1995, 3, 4). -connects(sp8_1995, 6, 5). -connects(sp8_1995, 7, 8). -connects(sp8_1995, 9, 10). -connects(sp8_1997, 2, 1). -connects(sp8_1997, 3, 4). -connects(sp8_1997, 6, 5). -connects(sp8_1997, 7, 8). -connects(sp8_1997, 9, 10). -connects(sp8_2001, 2, 1). -connects(sp8_2001, 4, 3). -connects(sp8_2001, 6, 5). -connects(sp8_2001, 7, 8). -connects(sp8_2001, 9, 10). -connects(sp8_3210, 2, 1). -connects(sp8_3210, 4, 3). -connects(sp8_3210, 6, 5). -connects(sp8_3210, 7, 8). -connects(sp8_3210, 10, 9). -connects(sp8_3214, 1, 2). -connects(sp8_3214, 4, 3). -connects(sp8_3214, 6, 5). -connects(sp8_3214, 7, 8). -connects(sp8_3214, 9, 10). -connects(sp8_3216, 2, 1). -connects(sp8_3216, 3, 4). -connects(sp8_3216, 6, 5). -connects(sp8_3216, 8, 7). -connects(sp8_3216, 9, 10). -connects(sp8_3218, 2, 1). -connects(sp8_3218, 3, 4). -connects(sp8_3218, 6, 5). -connects(sp8_3218, 7, 8). -connects(sp8_3218, 9, 10). -connects(sp8_5031, 2, 1). -connects(sp8_5031, 4, 3). -connects(sp8_5031, 6, 5). -connects(sp8_5031, 7, 8). -connects(sp8_5031, 9, 10). -connects(sp8_6324, 2, 1). -connects(sp8_6324, 4, 3). -connects(sp8_6324, 6, 5). -connects(sp8_6324, 7, 8). -connects(sp8_6324, 10, 9). -connects(sp8_6325, 2, 1). -connects(sp8_6325, 3, 4). -connects(sp8_6325, 6, 5). -connects(sp8_6325, 7, 8). -connects(sp8_6325, 9, 10). -connects(sp8_6338, 2, 1). -connects(sp8_6338, 3, 4). -connects(sp8_6338, 6, 5). -connects(sp8_6338, 7, 8). -connects(sp8_6338, 9, 10). -connects(sp8_6363, 2, 1). -connects(sp8_6363, 4, 3). -connects(sp8_6363, 6, 5). -connects(sp8_6363, 7, 8). -connects(sp8_6363, 9, 10). -connects(sp8_6366, 2, 1). -connects(sp8_6366, 4, 3). -connects(sp8_6366, 6, 5). -connects(sp8_6366, 7, 8). -connects(sp8_6366, 10, 9). -connects(sp8_6403, 2, 1). -connects(sp8_6403, 4, 3). -connects(sp8_6403, 6, 5). -connects(sp8_6403, 7, 8). -connects(sp8_6403, 10, 9). -connects(sp8_6409, 1, 2). -connects(sp8_6409, 4, 3). -connects(sp8_6409, 6, 5). -connects(sp8_6409, 8, 7). -connects(sp8_6409, 9, 10). -connects(sp8_6410, 1, 2). -connects(sp8_6410, 4, 3). -connects(sp8_6410, 6, 5). -connects(sp8_6410, 7, 8). -connects(sp8_6410, 10, 9). -connects(sp8_6684, 2, 1). -connects(sp8_6684, 4, 3). -connects(sp8_6684, 6, 5). -connects(sp8_6684, 8, 7). -connects(sp8_6684, 10, 9). -connects(emx2_1, 11, 12). -connects(emx2_1, 10, 13). -connects(emx2_1, 14, 3). -connects(emx2_3, 12, 11). -connects(emx2_3, 10, 13). -connects(emx2_3, 14, 3). -connects(emx2_4, 12, 11). -connects(emx2_4, 10, 13). -connects(emx2_4, 3, 14). -connects(emx2_5, 11, 12). -connects(emx2_5, 13, 10). -connects(emx2_5, 14, 3). -connects(emx2_19, 11, 12). -connects(emx2_19, 10, 13). -connects(emx2_19, 14, 3). -connects(emx2_28, 11, 12). -connects(emx2_28, 10, 13). -connects(emx2_28, 14, 3). -connects(emx2_58, 12, 11). -connects(emx2_58, 10, 13). -connects(emx2_58, 14, 3). -connects(emx2_1044, 12, 11). -connects(emx2_1044, 13, 10). -connects(emx2_1044, 14, 3). -connects(emx2_1058, 11, 12). -connects(emx2_1058, 10, 13). -connects(emx2_1058, 3, 14). -connects(emx2_1065, 11, 12). -connects(emx2_1065, 10, 13). -connects(emx2_1065, 3, 14). -connects(emx2_1393, 11, 12). -connects(emx2_1393, 10, 13). -connects(emx2_1393, 3, 14). -connects(emx2_1415, 12, 11). -connects(emx2_1415, 10, 13). -connects(emx2_1415, 3, 14). -connects(emx2_1417, 11, 12). -connects(emx2_1417, 10, 13). -connects(emx2_1417, 14, 3). -connects(emx2_1679, 11, 12). -connects(emx2_1679, 10, 13). -connects(emx2_1679, 3, 14). -connects(emx2_1997, 11, 12). -connects(emx2_1997, 10, 13). -connects(emx2_1997, 3, 14). -connects(emx2_2009, 11, 12). -connects(emx2_2009, 13, 10). -connects(emx2_2009, 3, 14). -connects(emx2_6325, 11, 12). -connects(emx2_6325, 10, 13). -connects(emx2_6325, 3, 14). -connects(emx2_6407, 12, 11). -connects(emx2_6407, 13, 10). -connects(emx2_6407, 14, 3). -connects(emx2_6409, 12, 11). -connects(emx2_6409, 10, 13). -connects(emx2_6409, 3, 14). -connects(emx2_6410, 11, 12). -connects(emx2_6410, 10, 13). -connects(emx2_6410, 14, 3). -connects(coupfti_1, 15, 8). -connects(coupfti_1, 6, 11). -connects(coupfti_1, 10, 16). -connects(coupfti_1, 2, 14). -connects(coupfti_1, 13, 17). -connects(coupfti_1, 4, 18). -connects(coupfti_3, 15, 8). -connects(coupfti_3, 11, 6). -connects(coupfti_3, 10, 16). -connects(coupfti_3, 2, 14). -connects(coupfti_3, 13, 17). -connects(coupfti_3, 18, 4). -connects(coupfti_4, 8, 15). -connects(coupfti_4, 11, 6). -connects(coupfti_4, 10, 16). -connects(coupfti_4, 14, 2). -connects(coupfti_4, 13, 17). -connects(coupfti_4, 4, 18). -connects(coupfti_6, 15, 8). -connects(coupfti_6, 11, 6). -connects(coupfti_6, 16, 10). -connects(coupfti_6, 14, 2). -connects(coupfti_6, 13, 17). -connects(coupfti_6, 18, 4). -connects(coupfti_9, 15, 8). -connects(coupfti_9, 6, 11). -connects(coupfti_9, 10, 16). -connects(coupfti_9, 2, 14). -connects(coupfti_9, 13, 17). -connects(coupfti_9, 4, 18). -connects(coupfti_12, 15, 8). -connects(coupfti_12, 6, 11). -connects(coupfti_12, 10, 16). -connects(coupfti_12, 2, 14). -connects(coupfti_12, 13, 17). -connects(coupfti_12, 4, 18). -connects(coupfti_28, 15, 8). -connects(coupfti_28, 11, 6). -connects(coupfti_28, 16, 10). -connects(coupfti_28, 2, 14). -connects(coupfti_28, 13, 17). -connects(coupfti_28, 18, 4). -connects(coupfti_44, 8, 15). -connects(coupfti_44, 11, 6). -connects(coupfti_44, 10, 16). -connects(coupfti_44, 2, 14). -connects(coupfti_44, 13, 17). -connects(coupfti_44, 4, 18). -connects(coupfti_78, 15, 8). -connects(coupfti_78, 6, 11). -connects(coupfti_78, 10, 16). -connects(coupfti_78, 14, 2). -connects(coupfti_78, 13, 17). -connects(coupfti_78, 4, 18). -connects(coupfti_97, 15, 8). -connects(coupfti_97, 6, 11). -connects(coupfti_97, 10, 16). -connects(coupfti_97, 14, 2). -connects(coupfti_97, 13, 17). -connects(coupfti_97, 4, 18). -connects(coupfti_128, 15, 8). -connects(coupfti_128, 6, 11). -connects(coupfti_128, 10, 16). -connects(coupfti_128, 14, 2). -connects(coupfti_128, 13, 17). -connects(coupfti_128, 4, 18). -connects(coupfti_132, 15, 8). -connects(coupfti_132, 11, 6). -connects(coupfti_132, 10, 16). -connects(coupfti_132, 14, 2). -connects(coupfti_132, 13, 17). -connects(coupfti_132, 18, 4). -connects(coupfti_764, 15, 8). -connects(coupfti_764, 6, 11). -connects(coupfti_764, 10, 16). -connects(coupfti_764, 14, 2). -connects(coupfti_764, 13, 17). -connects(coupfti_764, 4, 18). -connects(coupfti_1057, 15, 8). -connects(coupfti_1057, 6, 11). -connects(coupfti_1057, 10, 16). -connects(coupfti_1057, 14, 2). -connects(coupfti_1057, 13, 17). -connects(coupfti_1057, 4, 18). -connects(coupfti_1395, 15, 8). -connects(coupfti_1395, 6, 11). -connects(coupfti_1395, 10, 16). -connects(coupfti_1395, 14, 2). -connects(coupfti_1395, 13, 17). -connects(coupfti_1395, 4, 18). -connects(coupfti_1407, 8, 15). -connects(coupfti_1407, 11, 6). -connects(coupfti_1407, 16, 10). -connects(coupfti_1407, 2, 14). -connects(coupfti_1407, 17, 13). -connects(coupfti_1407, 18, 4). -connects(coupfti_1409, 15, 8). -connects(coupfti_1409, 11, 6). -connects(coupfti_1409, 10, 16). -connects(coupfti_1409, 14, 2). -connects(coupfti_1409, 13, 17). -connects(coupfti_1409, 4, 18). -connects(coupfti_1596, 15, 8). -connects(coupfti_1596, 11, 6). -connects(coupfti_1596, 10, 16). -connects(coupfti_1596, 14, 2). -connects(coupfti_1596, 13, 17). -connects(coupfti_1596, 4, 18). -connects(coupfti_1671, 15, 8). -connects(coupfti_1671, 11, 6). -connects(coupfti_1671, 10, 16). -connects(coupfti_1671, 14, 2). -connects(coupfti_1671, 13, 17). -connects(coupfti_1671, 18, 4). -connects(coupfti_1677, 15, 8). -connects(coupfti_1677, 6, 11). -connects(coupfti_1677, 10, 16). -connects(coupfti_1677, 14, 2). -connects(coupfti_1677, 13, 17). -connects(coupfti_1677, 4, 18). -connects(coupfti_2028, 15, 8). -connects(coupfti_2028, 6, 11). -connects(coupfti_2028, 10, 16). -connects(coupfti_2028, 2, 14). -connects(coupfti_2028, 13, 17). -connects(coupfti_2028, 4, 18). -connects(coupfti_2216, 15, 8). -connects(coupfti_2216, 6, 11). -connects(coupfti_2216, 10, 16). -connects(coupfti_2216, 14, 2). -connects(coupfti_2216, 13, 17). -connects(coupfti_2216, 4, 18). -connects(coupfti_2266, 15, 8). -connects(coupfti_2266, 11, 6). -connects(coupfti_2266, 10, 16). -connects(coupfti_2266, 2, 14). -connects(coupfti_2266, 13, 17). -connects(coupfti_2266, 18, 4). -connects(coupfti_2298, 8, 15). -connects(coupfti_2298, 11, 6). -connects(coupfti_2298, 10, 16). -connects(coupfti_2298, 14, 2). -connects(coupfti_2298, 13, 17). -connects(coupfti_2298, 4, 18). -connects(coupfti_3210, 15, 8). -connects(coupfti_3210, 11, 6). -connects(coupfti_3210, 10, 16). -connects(coupfti_3210, 14, 2). -connects(coupfti_3210, 13, 17). -connects(coupfti_3210, 4, 18). -connects(coupfti_3214, 15, 8). -connects(coupfti_3214, 11, 6). -connects(coupfti_3214, 10, 16). -connects(coupfti_3214, 14, 2). -connects(coupfti_3214, 13, 17). -connects(coupfti_3214, 4, 18). -connects(coupfti_3216, 15, 8). -connects(coupfti_3216, 11, 6). -connects(coupfti_3216, 10, 16). -connects(coupfti_3216, 14, 2). -connects(coupfti_3216, 13, 17). -connects(coupfti_3216, 18, 4). -connects(coupfti_3218, 15, 8). -connects(coupfti_3218, 11, 6). -connects(coupfti_3218, 16, 10). -connects(coupfti_3218, 14, 2). -connects(coupfti_3218, 13, 17). -connects(coupfti_3218, 18, 4). -connects(coupfti_3220, 15, 8). -connects(coupfti_3220, 6, 11). -connects(coupfti_3220, 10, 16). -connects(coupfti_3220, 14, 2). -connects(coupfti_3220, 13, 17). -connects(coupfti_3220, 4, 18). -connects(coupfti_5031, 15, 8). -connects(coupfti_5031, 6, 11). -connects(coupfti_5031, 10, 16). -connects(coupfti_5031, 2, 14). -connects(coupfti_5031, 13, 17). -connects(coupfti_5031, 4, 18). -connects(coupfti_6255, 15, 8). -connects(coupfti_6255, 6, 11). -connects(coupfti_6255, 10, 16). -connects(coupfti_6255, 2, 14). -connects(coupfti_6255, 13, 17). -connects(coupfti_6255, 4, 18). -connects(coupfti_6259, 15, 8). -connects(coupfti_6259, 6, 11). -connects(coupfti_6259, 10, 16). -connects(coupfti_6259, 14, 2). -connects(coupfti_6259, 13, 17). -connects(coupfti_6259, 4, 18). -connects(coupfti_6339, 8, 15). -connects(coupfti_6339, 11, 6). -connects(coupfti_6339, 10, 16). -connects(coupfti_6339, 2, 14). -connects(coupfti_6339, 13, 17). -connects(coupfti_6339, 18, 4). -connects(coupfti_6342, 8, 15). -connects(coupfti_6342, 11, 6). -connects(coupfti_6342, 16, 10). -connects(coupfti_6342, 14, 2). -connects(coupfti_6342, 13, 17). -connects(coupfti_6342, 18, 4). -connects(coupfti_6357, 15, 8). -connects(coupfti_6357, 11, 6). -connects(coupfti_6357, 16, 10). -connects(coupfti_6357, 2, 14). -connects(coupfti_6357, 13, 17). -connects(coupfti_6357, 18, 4). -connects(coupfti_6381, 8, 15). -connects(coupfti_6381, 11, 6). -connects(coupfti_6381, 10, 16). -connects(coupfti_6381, 2, 14). -connects(coupfti_6381, 13, 17). -connects(coupfti_6381, 4, 18). -connects(coupfti_6384, 15, 8). -connects(coupfti_6384, 11, 6). -connects(coupfti_6384, 10, 16). -connects(coupfti_6384, 2, 14). -connects(coupfti_6384, 13, 17). -connects(coupfti_6384, 18, 4). -connects(coupfti_6385, 15, 8). -connects(coupfti_6385, 11, 6). -connects(coupfti_6385, 16, 10). -connects(coupfti_6385, 2, 14). -connects(coupfti_6385, 13, 17). -connects(coupfti_6385, 18, 4). -connects(coupfti_6429, 8, 15). -connects(coupfti_6429, 11, 6). -connects(coupfti_6429, 10, 16). -connects(coupfti_6429, 2, 14). -connects(coupfti_6429, 13, 17). -connects(coupfti_6429, 4, 18). -connects(coupfti_6431, 15, 8). -connects(coupfti_6431, 11, 6). -connects(coupfti_6431, 16, 10). -connects(coupfti_6431, 2, 14). -connects(coupfti_6431, 13, 17). -connects(coupfti_6431, 18, 4). -connects(coupfti_6432, 15, 8). -connects(coupfti_6432, 11, 6). -connects(coupfti_6432, 16, 10). -connects(coupfti_6432, 2, 14). -connects(coupfti_6432, 13, 17). -connects(coupfti_6432, 18, 4). -connects(coupfti_6436, 8, 15). -connects(coupfti_6436, 11, 6). -connects(coupfti_6436, 10, 16). -connects(coupfti_6436, 2, 14). -connects(coupfti_6436, 13, 17). -connects(coupfti_6436, 4, 18). -connects(coupfti_6443, 15, 8). -connects(coupfti_6443, 11, 6). -connects(coupfti_6443, 16, 10). -connects(coupfti_6443, 2, 14). -connects(coupfti_6443, 13, 17). -connects(coupfti_6443, 18, 4). -connects(coupfti_6446, 8, 15). -connects(coupfti_6446, 11, 6). -connects(coupfti_6446, 10, 16). -connects(coupfti_6446, 2, 14). -connects(coupfti_6446, 13, 17). -connects(coupfti_6446, 4, 18). -connects(pax6_1, 15, 13). -connects(pax6_1, 12, 1). -connects(pax6_1, 9, 19). -connects(pax6_1, 2, 20). -connects(pax6_3, 15, 13). -connects(pax6_3, 1, 12). -connects(pax6_3, 19, 9). -connects(pax6_3, 2, 20). -connects(pax6_5, 13, 15). -connects(pax6_5, 12, 1). -connects(pax6_5, 9, 19). -connects(pax6_5, 20, 2). -connects(pax6_6, 15, 13). -connects(pax6_6, 12, 1). -connects(pax6_6, 9, 19). -connects(pax6_6, 20, 2). -connects(pax6_17, 15, 13). -connects(pax6_17, 12, 1). -connects(pax6_17, 9, 19). -connects(pax6_17, 2, 20). -connects(pax6_21, 15, 13). -connects(pax6_21, 12, 1). -connects(pax6_21, 9, 19). -connects(pax6_21, 2, 20). -connects(pax6_28, 13, 15). -connects(pax6_28, 1, 12). -connects(pax6_28, 9, 19). -connects(pax6_28, 2, 20). -connects(pax6_35, 15, 13). -connects(pax6_35, 1, 12). -connects(pax6_35, 9, 19). -connects(pax6_35, 2, 20). -connects(pax6_57, 15, 13). -connects(pax6_57, 12, 1). -connects(pax6_57, 19, 9). -connects(pax6_57, 20, 2). -connects(pax6_136, 15, 13). -connects(pax6_136, 12, 1). -connects(pax6_136, 19, 9). -connects(pax6_136, 20, 2). -connects(pax6_783, 15, 13). -connects(pax6_783, 12, 1). -connects(pax6_783, 19, 9). -connects(pax6_783, 2, 20). -connects(pax6_958, 15, 13). -connects(pax6_958, 12, 1). -connects(pax6_958, 19, 9). -connects(pax6_958, 20, 2). -connects(pax6_972, 15, 13). -connects(pax6_972, 12, 1). -connects(pax6_972, 19, 9). -connects(pax6_972, 20, 2). -connects(pax6_1000, 15, 13). -connects(pax6_1000, 12, 1). -connects(pax6_1000, 19, 9). -connects(pax6_1000, 20, 2). -connects(pax6_1049, 13, 15). -connects(pax6_1049, 12, 1). -connects(pax6_1049, 19, 9). -connects(pax6_1049, 20, 2). -connects(pax6_1059, 15, 13). -connects(pax6_1059, 12, 1). -connects(pax6_1059, 19, 9). -connects(pax6_1059, 20, 2). -connects(pax6_1407, 13, 15). -connects(pax6_1407, 1, 12). -connects(pax6_1407, 9, 19). -connects(pax6_1407, 2, 20). -connects(pax6_1997, 15, 13). -connects(pax6_1997, 12, 1). -connects(pax6_1997, 19, 9). -connects(pax6_1997, 20, 2). -connects(pax6_2021, 15, 13). -connects(pax6_2021, 12, 1). -connects(pax6_2021, 19, 9). -connects(pax6_2021, 20, 2). -connects(pax6_2142, 15, 13). -connects(pax6_2142, 12, 1). -connects(pax6_2142, 9, 19). -connects(pax6_2142, 20, 2). -connects(pax6_2266, 15, 13). -connects(pax6_2266, 12, 1). -connects(pax6_2266, 9, 19). -connects(pax6_2266, 20, 2). -connects(pax6_6261, 15, 13). -connects(pax6_6261, 12, 1). -connects(pax6_6261, 19, 9). -connects(pax6_6261, 2, 20). -connects(pax6_6262, 15, 13). -connects(pax6_6262, 12, 1). -connects(pax6_6262, 9, 19). -connects(pax6_6262, 20, 2). -connects(pax6_6265, 15, 13). -connects(pax6_6265, 1, 12). -connects(pax6_6265, 19, 9). -connects(pax6_6265, 2, 20). -connects(pax6_6325, 15, 13). -connects(pax6_6325, 12, 1). -connects(pax6_6325, 19, 9). -connects(pax6_6325, 20, 2). -connects(pax6_6326, 13, 15). -connects(pax6_6326, 12, 1). -connects(pax6_6326, 9, 19). -connects(pax6_6326, 20, 2). -connects(pax6_6347, 15, 13). -connects(pax6_6347, 12, 1). -connects(pax6_6347, 19, 9). -connects(pax6_6347, 20, 2). -connects(pax6_6348, 15, 13). -connects(pax6_6348, 12, 1). -connects(pax6_6348, 9, 19). -connects(pax6_6348, 20, 2). -connects(pax6_6349, 15, 13). -connects(pax6_6349, 12, 1). -connects(pax6_6349, 9, 19). -connects(pax6_6349, 20, 2). -connects(pax6_6393, 15, 13). -connects(pax6_6393, 12, 1). -connects(pax6_6393, 9, 19). -connects(pax6_6393, 20, 2). -connects(pax6_6395, 15, 13). -connects(pax6_6395, 12, 1). -connects(pax6_6395, 19, 9). -connects(pax6_6395, 2, 20). -connects(pax6_6398, 15, 13). -connects(pax6_6398, 12, 1). -connects(pax6_6398, 9, 19). -connects(pax6_6398, 2, 20). -connects(fgf8_1, 18, 16). -connects(fgf8_1, 21, 15). -connects(fgf8_4, 18, 16). -connects(fgf8_4, 15, 21). -connects(fgf8_6, 18, 16). -connects(fgf8_6, 15, 21). -connects(fgf8_20, 16, 18). -connects(fgf8_20, 21, 15). -connects(fgf8_759, 18, 16). -connects(fgf8_759, 21, 15). -connects(fgf8_1671, 18, 16). -connects(fgf8_1671, 21, 15). -connects(fgf8_2278, 16, 18). -connects(fgf8_2278, 21, 15). -connects(fgf8_6362, 18, 16). -connects(fgf8_6362, 15, 21). -connects(fgf8_6441, 18, 16). -connects(fgf8_6441, 15, 21). diff --git a/experiments/Synth/notebooks/pony_11_12_2024.jl b/experiments/Synth/notebooks/pony_11_12_2024.jl deleted file mode 100644 index e4600bd..0000000 --- a/experiments/Synth/notebooks/pony_11_12_2024.jl +++ /dev/null @@ -1,275 +0,0 @@ -### A Pluto.jl notebook ### -# v0.20.1 - -using Markdown -using InteractiveUtils - -# ╔═╡ 1575b3cc-b786-11ef-34ef-dd6f58044fcc -using DrWatson - -# ╔═╡ 0306d9f3-c210-4236-b9db-9ecb29787f08 -@quickactivate - -# ╔═╡ 8579e700-ef01-4ab9-a117-21bbf559d3dd -using GraphRecipes, Plots, DataFrames, Random, GraphDynamicalSystems, Graphs, MetaGraphsNext - -# ╔═╡ 63b200cc-ae46-468e-ab25-e6d6ab58874b -using DynamicalSystems: Attractors, trajectory, StateSpaceSets - -# ╔═╡ 90852030-deae-43d3-a573-124749d19d44 -md""" -# Synthesizing Graph Dynamical Systems - -PONY Lab Group Meeting, 11/12/2024 -""" - -# ╔═╡ 04361d06-11c1-468a-a97d-04a41b5d606a -md""" -## What's a GDS? - -A model represented by - -- A graph -- Each vertex in the graph has a function -- Edges in the graph denote which vertices are inputs to others -- Each vertex has a state -- Dynamics of the system depend on - - Vertices' functions - - The order you update them (_schedule_) -""" - -# ╔═╡ 4a95f701-cf79-4800-aa5a-72c6ee4c2e13 -md""" -## Example GDS: Boolean Network -""" - -# ╔═╡ e59b989c-2e88-4063-88d0-a2a4e3aae567 -md""" -## Why do we care about them? - -- Executable models for bio, for example -- Can use to study the effect of a drug on a biological system - -![BMA](https://github.com/user-attachments/assets/486bd9a9-a2a9-4dd3-8b55-00f7c8f1462a) -""" - -# ╔═╡ c01be032-ec3f-4b7b-9698-90c7555cac50 -md""" -## Why synthesize? - -- Constructing by hand is time consuming -- Basically amounts to guess-and-check - -To understand how we synthesize, let's return to our example network. What kind of information can we extract from it? -""" - -# ╔═╡ 2092c9f8-515e-4674-ad76-fdb2f22c0439 -md""" -## Data from our BN - -State space/single cell measurements -""" - -# ╔═╡ 9d09bc34-043c-4793-96f6-c7db927fed42 -md""" -Steady state/mutation experiments -""" - -# ╔═╡ 124b39da-e8a7-45d0-9a67-82dd08d16672 -md""" -## Types of Schedules - -- **Asynchronous** - - Choose a vertex at random from the network - - Run its function to update its value -- Synchronous - - Run all vertices' functions to update their values -- Bounded Asynchrony - - Run subsets of the vertices at the same time - - Tricky case, probably will ignore this for today -""" - -# ╔═╡ b608b534-4753-4529-8852-089aff34fab9 -md""" -## Async BN - -The `dynamic rule` is the asynchronous schedule mentioned before. -""" - -# ╔═╡ aab65a05-e256-4140-ae83-6547985e01a2 -md""" -## State Space -""" - -# ╔═╡ 04ed17cb-69bd-48eb-b294-af120fe26b19 -mg = MetaGraph(SimpleDiGraph(), label_type = String) - -# ╔═╡ 8955d1d1-3cd0-4691-9c7f-f6179cc6e048 -md""" -## Steady State(s) -""" - -# ╔═╡ bf0a7add-f668-4fcc-87d4-ad535bf93b12 -md""" -## Synthesizing - -From the two types of data, two kinds of specifications: -- State space specifies how a specific vertex's function should transform the state -- Steady state specifies how a collection of vertex functions should behave - -Refering to these as _local_ and _global_ constraints. -""" - -# ╔═╡ 80136458-da57-4ea9-943c-bd6692fad780 -md""" -## "Global" Constraint - -> The steady state of the model is `[1, 0, 0, 1, 1]` - -This is a constraint on the steady state of the model when combining synthesized functions for all vertices. -""" - -# ╔═╡ 606d1e0e-ee79-4b12-964a-c5d597508761 -md""" -## Discussion Question #1 - -If an incorrect steady-state is reached, which vertex is to blame? -""" - -# ╔═╡ 1f02a380-2f17-44d2-a0a7-e4f7f3974c4c -begin - mg2 = deepcopy(mg) - bad_v = string([1, 0, 1, 1, 1]) - add_vertex!(mg2, bad_v) - add_edge!(mg2, string([1, 0, 0, 1, 1]), bad_v) - plot( - mg2.graph; - names = mg2.vertex_labels, - nodesize = 0.06, - nodecolor = [:lightblue, :lightblue, :lightblue, :red], - ) -end - -# ╔═╡ a1759c77-2ff3-4722-a65c-25ecf50ac279 -md""" -## Discussion Question #2 - -Assuming a synchronous schedule, how can we reconstruct the state space like we saw before? -""" - -# ╔═╡ 8a243870-3d53-4312-b4d5-056f2372eb68 -md""" -## Extras -""" - -# ╔═╡ 5879d4f1-6bba-4675-b34b-9a1fcc023441 -seed = 42 - -# ╔═╡ a217cd51-8c4b-4e74-90d3-b8d22daa6d62 -Random.seed!(seed) - -# ╔═╡ 964fca0e-b28d-4ebd-a2ad-3daf2ba2d4cd -network = BooleanNetworks.sample_boolean_network(5, 3, seed) - -# ╔═╡ 452fda7d-e2f7-4162-a3d3-eef13ff3bdfc -plot(network.graph; names = network.vertex_labels, nodesize = 0.2) - -# ╔═╡ 230f34b5-c160-4f06-b560-8306ae62e51e -[(k => v[2]) for (k, v) in network.vertex_properties] - -# ╔═╡ d207a020-97e1-40ab-8f9b-8503cca9acf3 -plot(network.graph; names = network.vertex_labels, nodesize = 0.2) - -# ╔═╡ 51c331eb-77ef-4fcb-afff-c62fbf77d398 -async_bn = BooleanNetworks.abn(network) - -# ╔═╡ 6811c128-1aff-48e6-8af6-6946ebf7e6c1 -trjs = [trajectory(async_bn, 100) for _ = 1:1000] - -# ╔═╡ a52b6f33-bd36-4dae-9e90-4201e30bab78 -trjs[1][1][1:5] - -# ╔═╡ 8aeb938e-8cd2-4f0c-9bfc-9b35457b4e5d -trjs[1] - -# ╔═╡ a16f15fe-491d-43a4-b41f-5e3b82c0ec85 -ssp = begin - for t in trjs - for (f, s) in zip(t[1][1:end-1], t[1][2:end]) - add_vertex!(mg, string(f)) - add_vertex!(mg, string(s)) - add_edge!(mg, string(f), string(s)) - end - end - plot(mg.graph; names = mg.vertex_labels, nodesize = 0.05) -end - -# ╔═╡ d7a3067a-5b81-4409-bcf7-0254bcd591fb -ssp - -# ╔═╡ 8d39c39f-0897-43e8-942a-9a7884eb1dd4 -ssp - -# ╔═╡ 320ce9dc-5390-42eb-9b09-3e73f1bdd28b -attr = begin - grid = Tuple(range(0, 1) for _ = 1:5) - mapper = Attractors.AttractorsViaRecurrences(async_bn, grid) - for _ = 1:1000 - mapper(rand(0:1, 5)) - end - Attractors.extract_attractors(mapper) -end - -# ╔═╡ e99505e5-6697-42c5-b763-77e5ee3d0158 -attr[1] - -# ╔═╡ 743cb689-469e-48f1-82cc-f7e2abeadf9a -md""" -## Local Constraint - -> In: `[1, 0, 0, 1, 1]`, out: `[1, 0, 0, 0, 1]` - -This is a constraint on vertex `4`'s update function. - -A satisfying function would be $(string(network.vertex_properties[4][2])[26:end]) -""" - -# ╔═╡ Cell order: -# ╟─90852030-deae-43d3-a573-124749d19d44 -# ╟─04361d06-11c1-468a-a97d-04a41b5d606a -# ╟─4a95f701-cf79-4800-aa5a-72c6ee4c2e13 -# ╟─452fda7d-e2f7-4162-a3d3-eef13ff3bdfc -# ╟─230f34b5-c160-4f06-b560-8306ae62e51e -# ╟─e59b989c-2e88-4063-88d0-a2a4e3aae567 -# ╟─c01be032-ec3f-4b7b-9698-90c7555cac50 -# ╟─d207a020-97e1-40ab-8f9b-8503cca9acf3 -# ╟─2092c9f8-515e-4674-ad76-fdb2f22c0439 -# ╟─a52b6f33-bd36-4dae-9e90-4201e30bab78 -# ╟─9d09bc34-043c-4793-96f6-c7db927fed42 -# ╟─e99505e5-6697-42c5-b763-77e5ee3d0158 -# ╟─124b39da-e8a7-45d0-9a67-82dd08d16672 -# ╟─b608b534-4753-4529-8852-089aff34fab9 -# ╠═51c331eb-77ef-4fcb-afff-c62fbf77d398 -# ╟─aab65a05-e256-4140-ae83-6547985e01a2 -# ╟─6811c128-1aff-48e6-8af6-6946ebf7e6c1 -# ╠═8aeb938e-8cd2-4f0c-9bfc-9b35457b4e5d -# ╠═04ed17cb-69bd-48eb-b294-af120fe26b19 -# ╟─a16f15fe-491d-43a4-b41f-5e3b82c0ec85 -# ╟─8955d1d1-3cd0-4691-9c7f-f6179cc6e048 -# ╟─320ce9dc-5390-42eb-9b09-3e73f1bdd28b -# ╟─d7a3067a-5b81-4409-bcf7-0254bcd591fb -# ╟─bf0a7add-f668-4fcc-87d4-ad535bf93b12 -# ╟─743cb689-469e-48f1-82cc-f7e2abeadf9a -# ╟─80136458-da57-4ea9-943c-bd6692fad780 -# ╟─606d1e0e-ee79-4b12-964a-c5d597508761 -# ╟─1f02a380-2f17-44d2-a0a7-e4f7f3974c4c -# ╟─a1759c77-2ff3-4722-a65c-25ecf50ac279 -# ╟─8d39c39f-0897-43e8-942a-9a7884eb1dd4 -# ╟─8a243870-3d53-4312-b4d5-056f2372eb68 -# ╠═1575b3cc-b786-11ef-34ef-dd6f58044fcc -# ╠═0306d9f3-c210-4236-b9db-9ecb29787f08 -# ╠═a217cd51-8c4b-4e74-90d3-b8d22daa6d62 -# ╠═5879d4f1-6bba-4675-b34b-9a1fcc023441 -# ╠═964fca0e-b28d-4ebd-a2ad-3daf2ba2d4cd -# ╠═8579e700-ef01-4ab9-a117-21bbf559d3dd -# ╠═63b200cc-ae46-468e-ab25-e6d6ab58874b diff --git a/experiments/Synth/notebooks/why_qns.jl b/experiments/Synth/notebooks/why_qns.jl deleted file mode 100644 index e29e230..0000000 --- a/experiments/Synth/notebooks/why_qns.jl +++ /dev/null @@ -1,2784 +0,0 @@ -### A Pluto.jl notebook ### -# v0.20.4 - -using Markdown -using InteractiveUtils - -# ╔═╡ fe870bc6-2a75-11f0-1c8f-9d78fe7bfff9 -using DifferentialEquations - -# ╔═╡ 26547ad3-8b94-4c82-977f-041611e8dcbd -using ShortCodes - -# ╔═╡ 3a808c7b-1cf6-4430-8022-c03195428fbb -using Plots - -# ╔═╡ 6721b608-4191-45ff-bc30-167740781eb1 -H = 2 - -# ╔═╡ 4ceb2add-1215-49a6-8cb1-4ba21ebfc9f5 -f_act(u, K) = (u \ K)^H / (1 + (u / K)^H) - -# ╔═╡ 3014213a-1b39-4dea-afa5-ba48a0713458 -f_repr(u, K) = 1 / (1 + (u / K)^H) - -# ╔═╡ 4ceb8c6d-279e-45be-bad9-4564a7a516b0 -function make_dY(f_type) - (u, p, t) -> begin - (; B_y, β_y, X_star, K_xy, α_y) = p - B_y + β_y * f_type(X_star, K_xy) - α_y * u - end -end - -# ╔═╡ 6c8c03cf-5b98-44af-8533-dd97f5e330f8 -dY_act = make_dY(f_act) - -# ╔═╡ 85f34fa4-c8a4-4422-a7e9-4b4b7dd1026a -dY_repr = make_dY(f_repr) - -# ╔═╡ da738592-4fe3-438b-bc91-cdd704e7764e -G_and() - -# ╔═╡ f09cb7f4-efa5-416d-af07-ff0b81f72a73 -make_G(f_type) - -# ╔═╡ 167a79f3-0c74-445f-8591-aeaf05297f06 -function make_dZ(f_type, G_type) - (u, p, t) -> begin - (; B_z, β_z, X_star, K_xz, Y_star, K_yz, α_z) = p - B_z + β_z * G(X_star, K_xz, Y_star, K_yz) - α_z * u - end -end - -# ╔═╡ 2930c1d6-02c5-47b3-9d3f-e4ae05acf8b4 -# dZ(u, p, t) = p[:B_z] + p[:β_z] * G(p[:X_star], p[:K_xz], p[:Y_star], p[:K_yz]) - p[:α_z] * u - -# ╔═╡ 160e3a4e-ff33-4170-b2fc-4d20199d2bc1 -u0 = 1 / 2 - -# ╔═╡ 1e544c10-bb62-47fb-acaf-c9c394b8c397 -tspan = (0.0, 1.0) - -# ╔═╡ 2e437266-af8d-4e82-8241-1b7f20fbb5b8 -Markdown.MD( - md""" - From "Structure and Function of the Feed-Forward Loop Network Motif"[^1]: - - > In the figures, for FFL we use $H = 2$, $\beta_y = \beta_z = 1$, $\alpha_y = \alpha_z = 1$, $B_y = B_z = 0$; unless otherwise noted. For simple regulation we used $Y = 1$, $H = 2$, $\beta_z = 1$, $\alpha_z = 1$, $B_z = 0$, and $K_{xz} = 1$. - - Using their parameters here we can recreate their explorations into the behaviors of the different FFL-based motifs. - """, - Markdown.Footnote("1", DOI("10.1073/pnas.2133841100")), -) - -# ╔═╡ f69e7eb1-7129-464a-87f8-00961e14a503 -params_FFL = (α_y = 1, α_z = 1, β_y = 1, β_z = 1, B_y = 0, B_z = 0, K_xy = 1, X_star = 1) - -# ╔═╡ 601fb696-4059-4e52-8c3c-f3e5f6f1fb03 -params_simple = (Y = 1, H = 2, β_z = 1, α_z = 1, B_z = 0) - -# ╔═╡ 75a0607a-593e-4570-97f5-87794b173815 -prob = ODEProblem(dY_repr, u0, tspan, params_FFL) - -# ╔═╡ 3fcc1c1a-bade-42fd-91e2-47cfdf2fdce7 -sol = solve(prob, Tsit5(), reltol = 1e-8, abstol = 1e-8) - -# ╔═╡ a2325945-b5b6-4dc3-abd0-5ca28feb69b3 -plot( - sol, - linewidth = 5, - title = "Solution to the linear ODE with a thick line", - xaxis = "Time (t)", - yaxis = "u(t) (in μm)", - label = "My Thick Line!", -) # legend=false - -# ╔═╡ 16b3e935-a7d4-4a1d-9db2-88b9b3d8dbd7 -plot!(sol.t, t -> 0.5 * exp(1.01t), lw = 3, ls = :dash, label = "True Solution!") - 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-[[deps.x265_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" -uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" -version = "3.5.0+0" - -[[deps.xkbcommon_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] -git-tree-sha1 = "63406453ed9b33a0df95d570816d5366c92b7809" -uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "1.4.1+2" -""" - -# ╔═╡ Cell order: -# ╠═fe870bc6-2a75-11f0-1c8f-9d78fe7bfff9 -# ╠═26547ad3-8b94-4c82-977f-041611e8dcbd -# ╠═6721b608-4191-45ff-bc30-167740781eb1 -# ╠═4ceb2add-1215-49a6-8cb1-4ba21ebfc9f5 -# ╠═3014213a-1b39-4dea-afa5-ba48a0713458 -# ╠═4ceb8c6d-279e-45be-bad9-4564a7a516b0 -# ╠═6c8c03cf-5b98-44af-8533-dd97f5e330f8 -# ╠═85f34fa4-c8a4-4422-a7e9-4b4b7dd1026a -# ╠═da738592-4fe3-438b-bc91-cdd704e7764e -# ╠═f09cb7f4-efa5-416d-af07-ff0b81f72a73 -# ╠═167a79f3-0c74-445f-8591-aeaf05297f06 -# ╠═2930c1d6-02c5-47b3-9d3f-e4ae05acf8b4 -# ╠═160e3a4e-ff33-4170-b2fc-4d20199d2bc1 -# ╠═1e544c10-bb62-47fb-acaf-c9c394b8c397 -# ╟─2e437266-af8d-4e82-8241-1b7f20fbb5b8 -# ╠═f69e7eb1-7129-464a-87f8-00961e14a503 -# ╠═601fb696-4059-4e52-8c3c-f3e5f6f1fb03 -# ╠═75a0607a-593e-4570-97f5-87794b173815 -# ╠═3fcc1c1a-bade-42fd-91e2-47cfdf2fdce7 -# ╠═3a808c7b-1cf6-4430-8022-c03195428fbb -# ╠═a2325945-b5b6-4dc3-abd0-5ca28feb69b3 -# ╠═16b3e935-a7d4-4a1d-9db2-88b9b3d8dbd7 -# ╟─00000000-0000-0000-0000-000000000001 -# ╟─00000000-0000-0000-0000-000000000002 diff --git a/experiments/Synth/scripts/benchmark.jl b/experiments/Synth/scripts/benchmark.jl deleted file mode 100644 index 322a678..0000000 --- a/experiments/Synth/scripts/benchmark.jl +++ /dev/null @@ -1,83 +0,0 @@ -using DrWatson - -@quickactivate :Synth - -using BenchmarkTools -using SoleLogics -using MLStyle - -import SoleLogics.interpret - -function interpret(φ::Expr, i::SoleLogics.AbstractInterpretation, args...; kwargs...) - syntax_branch = @match φ begin - Expr(:call, :Atom, val) => interpret(Atom(val), i, args...; kwargs...) - Expr(:call, :∨, children...) => interpret( - SyntaxBranch(∨, [interpret(ch, i, args...; kwargs...) for ch in children]...), - i, - args...; - kwargs..., - ) - Expr(:call, :∧, children...) => interpret( - SyntaxBranch(∧, [interpret(ch, i, args...; kwargs...) for ch in children]...), - i, - args...; - kwargs..., - ) - Expr(:call, :¬, child) => interpret( - SyntaxBranch(¬, interpret(child, i, args...; kwargs...)), - i, - args...; - kwargs..., - ) - _ => error("missing a match for $φ") - end - - return interpret(syntax_branch, i, args...; kwargs...) -end - -function benchmark() - exprs = [ - :( - Atom(0) ∨ Atom(1) ∨ Atom(1) ∨ Atom(1) ∨ Atom(1) ∨ Atom(1) ∨ Atom(1) ∨ Atom(1) ∨ Atom(1) ∨ Atom(1) - ), - :(Atom("a") ∨ Atom("b")), - :( - (¬Atom("v_cGSH_GSSG_b1") ∧ Atom("v_cGR_b1")) ∨ - (Atom("v_cGSH_GSSG_b1") ∧ ¬Atom("v_cGSH_GSSG_b2") ∧ Atom("v_cGR_b1")) ∨ - (Atom("v_cGSH_GSSG_b1") ∧ Atom("v_cGSH_GSSG_b2")) - ), - :((¬Atom(1) ∧ Atom(2)) ∨ (Atom(3) ∧ ¬Atom(4) ∧ Atom(5)) ∨ (Atom(6) ∧ Atom(7))), - ] - - for expr in exprs - syntax_branch = eval(expr) - - td = TruthDict([0 => ⊤, 1 => ⊤]) - - eval_version = @benchmarkable interpret(eval($expr), $td) - custom_eval_version = @benchmarkable interpret($expr, $td) - no_eval_version = @benchmarkable interpret($syntax_branch, $td) - - @info "Tuning" - tune!(eval_version) - tune!(custom_eval_version) - tune!(no_eval_version) - - @info "Running" - m_eval = median(run(eval_version)) - m_custom_eval = median(run(custom_eval_version)) - m_no_eval = median(run(no_eval_version)) - - @info "Normal Eval vs. No Eval at all" - @info judge(m_eval, m_no_eval) - - @info "Custom interpret without eval vs normal interpret without eval" - @info judge(m_custom_eval, m_no_eval) - - @info "Custom interpret vs. just eval" - @info judge(m_custom_eval, m_eval) - end - -end - -benchmark() diff --git a/experiments/Synth/scripts/convert_asp.jl b/experiments/Synth/scripts/convert_asp.jl deleted file mode 100644 index d0e8bd1..0000000 --- a/experiments/Synth/scripts/convert_asp.jl +++ /dev/null @@ -1,23 +0,0 @@ -"entity($(join(only(unique(df.vertex_names)), ";")))." - -eas = only(df[df.index_of_vertex.==1, :exprs_and_scores]) - -# get state space that solutions create -Set(getindex.(eas, 3)) - -# get states -Dict([i => x for (i, x) in enumerate(Set(hcat(collect.(df.selected_trajectories[1])...)))]) - -states = df.selected_trajectories |> Iterators.flatten |> Iterators.flatten |> collect -ids = eachindex(states) -"state(1..$(ids.stop))." - -# Get the - -for row in eachrow(df) - for (expr, score, topology, idx) in row.exprs_and_scores - @show expr, topology - - end - break -end diff --git a/experiments/Synth/scripts/get_biodivine_benchmark.jl b/experiments/Synth/scripts/get_biodivine_benchmark.jl deleted file mode 100644 index bf181d2..0000000 --- a/experiments/Synth/scripts/get_biodivine_benchmark.jl +++ /dev/null @@ -1,43 +0,0 @@ -#!/usr/bin/env -S julia --project=. -# -#SBATCH --job-name="BBM" -#SBATCH --partition=compute -#SBATCH --time=00:15:00 -#SBATCH --ntasks 32 -#SBATCH --cpus-per-task=1 -#SBATCH --mem-per-cpu=1G -#SBATCH --account=research-eemcs-st - -using Distributed -try - using SlurmClusterManager - addprocs(SlurmManager()) -catch - @info "Not running from within Slurm, proceeding without Slurm workers" -end - -@everywhere using DrWatson - -@everywhere quickactivate(pwd()) -@everywhere using Synth - - -@info "Cloning the biodivine benchmark repository" -bbm_dir = datadir("src_raw", "biodivine-boolean-models") -if !isdir(bbm_dir) - get_biodivine_repo(bbm_dir) -end - -@info "Bundling the benchmark to .aeon format" -aeon_bundle_dir = joinpath(bbm_dir, "bbm-aeon-format") -if !isdir(aeon_bundle_dir) - bundle_biodivine_benchmark(bbm_dir, aeon_bundle_dir) -end - -@info "Parsing .aeon model files" -ids_to_ignore = ["079"] -load_aeon_biodivine(bbm_dir, ids_to_ignore) - -@info "Converting to MetaGraph-based models" -excluded_files = [r"041\.aeon\.jld2", r"079\.aeon\.jld2"] -convert_aeon_models_to_metagraphs(excluded_files) diff --git a/experiments/Synth/scripts/make_zip_all.sh b/experiments/Synth/scripts/make_zip_all.sh deleted file mode 100644 index 731ddee..0000000 --- a/experiments/Synth/scripts/make_zip_all.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash - -#SBATCH --job-name=makezipall -#SBATCH --partition=compute -#SBATCH --time=02:00:00 -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=1 -#SBATCH --mem-per-cpu=4G -#SBATCH --account=innovation - -# Load modules: -module load 2024r1 - -zip -r /scratch/rjgardosreid/Synth_backup.zip data/ diff --git a/experiments/Synth/scripts/pluto_session.sh b/experiments/Synth/scripts/pluto_session.sh deleted file mode 100644 index 766c475..0000000 --- a/experiments/Synth/scripts/pluto_session.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -#SBATCH --job-name=pluto -#SBATCH --partition=compute -#SBATCH --time=02:00:00 -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=4 -#SBATCH --mem-per-cpu=2G -#SBATCH --account=innovation - -# Load modules: -module load 2024r1 -module load julia - -echo Starting Pluto 🎈 - -srun julia --project=. -e 'using Pluto; Pluto.run(host="0.0.0.0", port=1234, launch_browser=false)' > pluto_output.txt diff --git a/experiments/Synth/scripts/profile_biodivine_synth.jl b/experiments/Synth/scripts/profile_biodivine_synth.jl deleted file mode 100644 index 3f5dfaa..0000000 --- a/experiments/Synth/scripts/profile_biodivine_synth.jl +++ /dev/null @@ -1,89 +0,0 @@ -using DrWatson - -@quickactivate :Synth - -using DataFrames -using Herb, HerbGrammar, SoleLogics, HerbSpecification, HerbSearch -using Random -using Graphs: nv - -include(srcdir("grammars.jl")) -include(srcdir("synth_process.jl")) -include(srcdir("evaluator.jl")) -include(srcdir("create_problem.jl")) - -res = collect_results!(datadir("sims", "biodivine_split")) -res.ID = ((x -> x[end-1]["id"]) ∘ parse_savename).(res.path) -rename!(res, :path => "Trajectory Path") -mg_df = collect_results!(datadir("src_parsed", "biodivine_benchmark_as_metagraphs")) -mg_df.ID = ((x -> parse(Int, x)) ∘ (x -> x[1]) ∘ splitext ∘ basename).(mg_df.path) -rename!(mg_df, :path => "Model Path") - -res = innerjoin(res, mg_df, on = :ID) - -synth_params = Dict( - "seed" => 42, - "max_depth" => 6, - "id" => res.ID, - "n_trajectories" => collect(100:100:100), - "iterator_type" => [BFSIterator], - "grammar_builder" => [build_dnf_grammar], - "max_iterations" => 100, -) - -function synth_one_vertex(save_data) - @unpack vertex, examples = save_data - problem = examples_to_problem(vertex, examples) - - @unpack max_depth, iterator_type, max_iterations, grammar = save_data - iterator = iterator_type(grammar, :Start, max_depth = max_depth) - exprs_and_scores = synth_biodivine(problem, iterator, grammar, max_iterations) - - # Save output - save_data["exprs_and_scores"] = exprs_and_scores - return save_data -end - -function synth_one_biodivine(outer_params::AbstractDict{String,Any}, res::DataFrame) - params = deepcopy(outer_params) - @unpack seed = params - Random.seed!(seed) - - @unpack n_trajectories = params - selected_trajs = rand(res.split_traj[1], n_trajectories) - - merged_selected_trajs = reduce(mergewith(union), selected_trajs) - - @unpack id, grammar_builder = params - @info "Synthsizing for model $id with $n_trajectories traj." - - model = only(res[res.ID.==id, :metagraph_model]) - grammar = grammar_builder(nv(model)) - - for (vertex, examples) ∈ merged_selected_trajs - @info "Synthesizing model $id, node $vertex, $n_trajectories traj." - save_data = deepcopy(params) - delete!(save_data, "specifications") - save_data["grammar"] = grammar - save_data["vertex"] = vertex - save_data["examples"] = examples - file_name = savename(save_data) - @produce_or_load( - synth_one_vertex, - save_data, - datadir("exp_raw", "biodivine_search"); - filename = file_name, - force = true - ) - @info "Completed synthesis for model $id, node $vertex, $n_trajectories traj." - end -end - -function to_profile(synth_params, res) - for params in dict_list(synth_params) - synth_one_biodivine(params, res) - end -end - -@profview to_profile(synth_params, res) -@profview to_profile(synth_params, res) diff --git a/experiments/Synth/scripts/sample_trajectories_biodivine.jl b/experiments/Synth/scripts/sample_trajectories_biodivine.jl deleted file mode 100644 index 85929eb..0000000 --- a/experiments/Synth/scripts/sample_trajectories_biodivine.jl +++ /dev/null @@ -1,47 +0,0 @@ -#!/usr/bin/env -S julia --project=. -# -#SBATCH --job-name="Trajectories" -#SBATCH --partition=compute -#SBATCH --time=02:00:00 -#SBATCH --ntasks 32 -#SBATCH --cpus-per-task=1 -#SBATCH --mem-per-cpu=8G -#SBATCH --account=research-eemcs-st - -using Distributed -try - using SlurmClusterManager - addprocs(SlurmManager()) -catch - @info "Not running from within Slurm, proceeding without Slurm workers" -end - -@everywhere using DrWatson - -@everywhere quickactivate(pwd()) -@everywhere using Synth - -using DataFrames -using GraphDynamicalSystems: BooleanNetworks -using ProgressMeter -using MetaGraphsNext - -mg_df = collect_results(datadir("src_parsed", "biodivine_benchmark_as_metagraphs")) -mg_df[!, :ID] = ((x -> x[1]) ∘ splitext ∘ basename).(mg_df.path) -mg_df_by_id = groupby(mg_df, :ID) - -param_setup = Dict("id" => mg_df.ID, "n_trajectories" => 200, "iterations" => 100) -all_params = dict_list(param_setup) - -@showprogress pmap(all_params) do params - @produce_or_load(params, path = datadir("sims", "biodivine_trajectories"),) do params - @unpack id, n_trajectories, iterations = params - bn = mg_df_by_id[(id,)].metagraph_model[1] - trajectories = [] - for traj_i = 1:n_trajectories - async_bn = BooleanNetworks.abn(bn; seed = traj_i) - push!(trajectories, gather_bn_data(async_bn, iterations)) - end - @strdict trajectories - end -end diff --git a/experiments/Synth/scripts/split_biodivine_trajectories.jl b/experiments/Synth/scripts/split_biodivine_trajectories.jl deleted file mode 100644 index 8c74dc5..0000000 --- a/experiments/Synth/scripts/split_biodivine_trajectories.jl +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env -S julia --project=. -# -#SBATCH --job-name="Split" -#SBATCH --partition=compute -#SBATCH --time=02:00:00 -#SBATCH --ntasks 64 -#SBATCH --cpus-per-task=1 -#SBATCH --mem-per-cpu=8G -#SBATCH --account=research-eemcs-st - -using Distributed -try - using SlurmClusterManager - addprocs(SlurmManager()) -catch - @info "Not running from within Slurm, proceeding without Slurm workers" -end - -@everywhere using DrWatson - -@everywhere quickactivate(pwd()) -@everywhere using Synth - -using JLD2 -using ProgressMeter - -trajectories_dir = datadir("sims", "biodivine_trajectories") -split_trajectories_dir = datadir("sims", "biodivine_split") - -param_setup = Dict("traj_file" => readdir(datadir("sims", "biodivine_trajectories"))) -all_params = dict_list(param_setup) - -@showprogress pmap(all_params) do params - @produce_or_load( - params, - path = split_trajectories_dir, - filename = splitext(basename(params["traj_file"]))[1] - ) do params - @unpack traj_file = params - traj_file_contents = load(joinpath(trajectories_dir, traj_file)) - split_traj = nothing - if "trajectories" in keys(traj_file_contents) - trajectories = traj_file_contents["trajectories"] # also includes git tag, etc. - - split_traj = split_state_space.(trajectories) - end - @strdict split_traj - end -end diff --git a/experiments/Synth/scripts/synth_biodivine.jl b/experiments/Synth/scripts/synth_biodivine.jl deleted file mode 100644 index 4f43371..0000000 --- a/experiments/Synth/scripts/synth_biodivine.jl +++ /dev/null @@ -1,102 +0,0 @@ -#!/usr/bin/env -S julia --project=. -# -#SBATCH --job-name="Synth" -#SBATCH --partition=compute -#SBATCH --time=02:00:00 -#SBATCH --ntasks 256 -#SBATCH --cpus-per-task=1 -#SBATCH --mem-per-cpu=8G -#SBATCH --account=research-eemcs-st - -using Distributed -try - using SlurmClusterManager - addprocs(SlurmManager()) -catch - @info "Not running from within Slurm, proceeding without Slurm workers" -end - -@everywhere using DrWatson - -@everywhere quickactivate(pwd()) -@everywhere using Synth - -@everywhere using ProgressMeter, DataFrames, HerbSearch, GraphDynamicalSystems, Random -using MetaGraphsNext: labels -using Statistics: quantile - -traj_df = collect_results(datadir("sims", "biodivine_split")) -path2id = path -> parse_savename(path)[end-1]["id"] -traj_df.ID = path2id.(traj_df.path) - -model_df = collect_results(datadir("src_parsed", "biodivine_benchmark_as_metagraphs");) -path2id = path -> parse(Int, splitext(basename(path))[1]) -model_df.ID = path2id.(model_df.path) -model_df.vertex = collect.(labels.(model_df.metagraph_model)) -# add a copy so that after flattening we have all of the vertices of a model in each row of df -model_df.vertices = model_df.vertex - -# Filter out the largest 5% of models -# They are likely Booleanized multivalue models—have to check -n_verts_per_model = length.(model_df.vertices) -per_vertex_df = - flatten(model_df[n_verts_per_model.<=quantile(n_verts_per_model, 0.95), :], :vertex) - -grammars_df = model_df[!, [:ID, :vertices]] -grammars_df.dnf_grammar = build_dnf_grammar.(grammars_df.vertices) -grammars_df.qn_grammar = build_qn_grammar.(grammars_df.vertices) - -get_evaluator = g -> Dict([:DNF => evaluate_bn, :QN => evaluate_qn])[g] - -function get_grammar(unique, grammar_type) - s = :unknown - if grammar_type == :DNF - s = :dnf_grammar - elseif grammar_type == :QN - s = :qn_grammar - end - - return only(grammars_df[grammars_df.ID.==unique.ID, s]) -end - -function select_trajectories(df, N, id, vertex, seed) - Random.seed!(seed) - selected_trajectories = rand(only(df[df.ID.==id, :split_traj]), N) - filtered_on_vertex = - reduce(union, map(x -> get(x, vertex, Set()), selected_trajectories)) - - return filtered_on_vertex -end - -synth_params = Dict( - "seed" => 42, - "max_depth" => 7, - "unique" => collect(eachrow(per_vertex_df[!, [:ID, :vertex, :vertices]])), - # "metagraph_model" => Derived("unique", x -> first(model_df[model_df.ID .== x.ID].metagraph_model), - "id" => Derived("unique", x -> x.ID), - "vertex_names" => Derived("unique", x -> getfield.(x.vertices, :value)), - "index_of_vertex" => Derived("unique", x -> findfirst(==(x.vertex), x.vertices)), - "vertex" => Derived("unique", x -> string(x.vertex.value)), - "n_trajectories" => 10, #collect(10:45:110), - "selected_trajectories" => Derived( - ["n_trajectories", "unique", "index_of_vertex", "seed"], - (N, unique, index_of_vertex, seed) -> - select_trajectories(traj_df, N, unique.ID, index_of_vertex, seed), - ), - "iterator_type" => [BFSIterator], - "iter_name" => Derived("iterator_type", string), - "grammar_type" => :QN, #[:DNF, :QN], - "grammar" => Derived(["unique", "grammar_type"], get_grammar), - "evaluator" => Derived("grammar_type", get_evaluator), - "max_iterations" => 10_000, -) - -@showprogress pmap(dict_list(synth_params)) do params - # loadfile = false so we don't load all results into memory of the main process - @produce_or_load( - synth_one_vertex, - params, - datadir("exp_raw", "biodivine_search"), - loadfile = false - ) -end diff --git a/experiments/Synth/scripts/synth_biodivine.sh b/experiments/Synth/scripts/synth_biodivine.sh deleted file mode 100644 index 97bea23..0000000 --- a/experiments/Synth/scripts/synth_biodivine.sh +++ /dev/null @@ -1,12 +0,0 @@ -#!/bin/sh -# -#SBATCH --job-name="Synthesize Biodivine" -#SBATCH --partition=compute -#SBATCH --time=24:00:00 -#SBATCH --cpus-per-task=32 -#SBATCH --mem-per-cpu=3G -#SBATCH --account=innovation - -module load 2024r1 julia - -srun julia -t 32 --project=. scripts/synth_biodivine.jl diff --git a/experiments/Synth/scripts/synthesize_bn.jl b/experiments/Synth/scripts/synthesize_bn.jl deleted file mode 100644 index 3f00ed3..0000000 --- a/experiments/Synth/scripts/synthesize_bn.jl +++ /dev/null @@ -1,58 +0,0 @@ -using DrWatson - -@quickactivate "Synth" - -using DataFrames -using Herb, HerbGrammar, SoleLogics, HerbSpecification, HerbSearch - -include(srcdir("gather_bn_data.jl")) # So that all dependencies are also loaded when loading results -include(srcdir("grammars.jl")) - -include(srcdir("synth_process.jl")) -include(srcdir("evaluator.jl")) -include(srcdir("create_problem.jl")) - -res = collect_results!(datadir("sims", "specs")) - -cnf_grammar = build_cnf_grammar(res.network_size[1]) -dnf_grammar = build_dnf_grammar(res.network_size[1]) - -synth_params = Dict( - "seed" => res.seed, - "specifications" => Derived( - "seed", - seed -> filter(row -> row.seed == seed, res)[!, :specifications][1], - ), - "break_after" => Inf, - "max_neighbors" => 3, - "max_depth" => 7, - "max_iterations" => 100_000, - "grammar" => [cnf_grammar, dnf_grammar], - "grammar_type" => Derived("grammar", x -> x == cnf_grammar ? "cnf" : "dnf"), - "iterator_type" => BFSIterator, -) - -Threads.@threads for params in dict_list(synth_params) - @unpack seed, grammar_type, specifications = params - @info "Synthesizing for seed $seed, grammar $grammar_type" - - # Create Problem for each node - for (node, examples) in specifications - problem = examples_to_problem(node, examples) - - # Synthesize - @unpack grammar, max_neighbors, max_iterations = params - @unpack max_depth, iterator_type = params - iterator = iterator_type(grammar, :Start, max_depth = max_depth) - exprs_and_scores = synth(problem, iterator, grammar, max_neighbors, max_iterations) - - # Save output - save_data = copy(params) - delete!(save_data, "specifications") - save_data["node"] = node - save_data["examples"] = examples - save_data["exprs_and_scores"] = exprs_and_scores - file_name = savename(save_data, "cnf_search.jld2") - @tagsave(datadir("exp_raw", "cnf_search", file_name), save_data) - end -end diff --git a/experiments/Synth/src/Synth.jl b/experiments/Synth/src/Synth.jl deleted file mode 100644 index a1c7103..0000000 --- a/experiments/Synth/src/Synth.jl +++ /dev/null @@ -1,46 +0,0 @@ -module Synth - -using DrWatson -using HerbGrammar -using GraphDynamicalSystems -using HerbSpecification - -include("parse_aeon.jl") - -export AEONParser - -include("biodivine_benchmark.jl") - -export load_aeon_biodivine, - get_biodivine_repo, - bundle_biodivine_benchmark, - update_functions_to_network, - convert_aeon_models_to_metagraphs, - synth_one_biodivine, - synth_one_vertex - -include("grammars.jl") - -export build_cnf_grammar, build_dnf_grammar, count_neighbors_in_expr - -include("gather_bn_data.jl") - -export gather_bn_data, split_state_space, get_split_state_space - -include("synth_process.jl") - -export synth, synth_biodivine, synth_complete_qn, get_basins_qn, get_basins_bn - -include("undirected_specification.jl") - -export UndirectedExample, UndirectedProblem - -include("evaluator.jl") - -export evaluate_bn, evaluate_qn, interpret - -include("create_problem.jl") - -export examples_to_problem - -end diff --git a/experiments/Synth/src/biodivine_benchmark.jl b/experiments/Synth/src/biodivine_benchmark.jl deleted file mode 100644 index eb2fd22..0000000 --- a/experiments/Synth/src/biodivine_benchmark.jl +++ /dev/null @@ -1,162 +0,0 @@ -using CondaPkg -using Git - -using DataFrames, CSV - -import GraphDynamicalSystems.BooleanNetworks: update_functions_to_network -using MetaGraphsNext: MetaGraph -using Graphs: SimpleDiGraph, add_edge! -using SoleLogics: Atom, subformulas, Formula - -using ProgressMeter - -using TidierData - -using Distributed -using Random -using Graphs: nv - -function get_biodivine_repo(raw_src_dir) - remote = "https://github.com/ReubenJ/biodivine-boolean-models.git" - commit_hash = "f785e571308122378664d0ad4168969cb70cdcc2" - checkout_cmd = `$(git()) checkout $commit_hash` - - if ispath(raw_src_dir) - cd(raw_src_dir) - try - run(`$(git()) pull`) - run(checkout_cmd) - catch - @error "Error pulling from git remote, see git output above." - end - else - run(`$(git()) clone $remote $raw_src_dir`) - cd(raw_src_dir) - run(checkout_cmd) - end -end - -function bundle_biodivine_benchmark(raw_src_dir, output_dir) - cd(raw_src_dir) - CondaPkg.withenv() do - run( - `python bundle.py --format aeon --inputs free --filter "" --output-dir $output_dir`, - ) - end -end - -function load_aeon_biodivine(repo, ids_to_ignore = String[]) - @assert ispath(repo) - - models_path = joinpath(repo, "bbm-aeon-format") - summary_csv_path = joinpath(models_path, "summary.csv") - - df = DataFrame( - CSV.File(summary_csv_path; types = Dict([:ID => String]), normalizenames = true), - ) - - # construct path column - df = transform(df, :ID => ByRow(id -> joinpath(models_path, id) * ".aeon") => :path) - - @tagsave(datadir("src_parsed", "summary_biodivine_benchmark.jld2"), @strdict(df)) - - df = filter(row -> row.ID ∉ ids_to_ignore, df) - - @showprogress "Parsing AEON Files..." pmap(eachrow(df)) do model - @produce_or_load( - model, - path = datadir("src_parsed", "biodivine_benchmark"), - filename = basename(model.path), - verbose = false - ) do model - parsed_model = AEONParser.parse_aeon_file(model.path) - @strdict parsed_model - end - end -end - -function update_functions_to_network( - update_functions::AbstractVector{<:AEONParser.UpdateFunction}, - regulations::AbstractVector{<:AEONParser.Regulation}, -) - network = MetaGraph(SimpleDiGraph(); label_type = Atom, vertex_data_type = Formula) - - # By default let every node's update function equal itself - # This means applying an update won't do anything - # Should've chosen `identity` for inputs when bundling the benchmark - for reg in regulations - network[Atom(reg.regulator.name)] = Atom(reg.regulator.name) - network[Atom(reg.target.name)] = Atom(reg.target.name) - end - - # Then for any node that does have an update function, we assign it here - for up in update_functions - network[Atom(String(up.target.name))] = up.fn - end - - for up in update_functions - atoms = Set(Leaves(up.fn)) - for atom in atoms - source = atom.value - add_edge!(network, up.target.name, source) - end - end - - return network -end - -function convert_aeon_models_to_metagraphs(excluded_files = Regex[]) - df = collect_results( - datadir("src_parsed", "biodivine_benchmark"); - rexclude = excluded_files, - ) - # "full/path/to/001.aeon.jld2" -> "001" - df.ID = map((x -> x[1]) ∘ splitext ∘ (x -> x[1]) ∘ splitext ∘ basename, df.path) - - gdf = @chain df begin - @select parsed_model ID - flatten(:parsed_model) - @rename Component = parsed_model # new = old - @mutate ComponentType = typeof(Component) - @group_by ID - end - - @showprogress "AEON -> MetaGraph" pmap(pairs(gdf)) do (components_key, components) - @produce_or_load( - components, - path = datadir("src_parsed", "biodivine_benchmark_as_metagraphs"), - filename = components_key.ID - ) do components - metagraph_model = update_functions_to_network( - Vector{AEONParser.UpdateFunction}( - components[ - components.ComponentType.==AEONParser.UpdateFunction, - :, - ].Component, - ), - Vector{AEONParser.Regulation}( - components[ - components.ComponentType.==AEONParser.Regulation, - :, - ].Component, - ), - ) - @strdict metagraph_model - end - end -end - -function synth_one_vertex(params::AbstractDict{String,Any}) - @unpack vertex, selected_trajectories = params - problem = examples_to_problem(vertex, selected_trajectories) - - @unpack max_depth, iterator_type, max_iterations, grammar, evaluator, vertex_names = - params - iterator = iterator_type(grammar, :Start, max_depth = max_depth) - exprs_and_scores = - synth_biodivine(problem, iterator, grammar, max_iterations, evaluator, vertex_names) - - # Save output - params["exprs_and_scores"] = exprs_and_scores - return params -end diff --git a/experiments/Synth/src/create_problem.jl b/experiments/Synth/src/create_problem.jl deleted file mode 100644 index 4592a22..0000000 --- a/experiments/Synth/src/create_problem.jl +++ /dev/null @@ -1,9 +0,0 @@ -function _e2p(n, e) - _pair_to_undirected(p) = UndirectedExample(Dict(:state => p[1]), Dict(:state => p[2])) - - return UndirectedProblem(n, _pair_to_undirected.(e)) -end - -examples_to_problem(node::Integer, examples::AbstractSet) = _e2p(string(node), examples) -examples_to_problem(node::Atom, examples) = _e2p(node.value, examples) -examples_to_problem(node::AbstractString, examples) = _e2p(node, examples) diff --git a/experiments/Synth/src/evaluator.jl b/experiments/Synth/src/evaluator.jl deleted file mode 100644 index 6ecc581..0000000 --- a/experiments/Synth/src/evaluator.jl +++ /dev/null @@ -1,101 +0,0 @@ -using MLStyle: @match -using SoleLogics -import SoleLogics.interpret - -function interpret(φ::Expr, i::SoleLogics.AbstractInterpretation, args...; kwargs...) - syntax_branch = @match φ begin - Expr(:call, :Atom, val) => interpret(Atom(val), i, args...; kwargs...) - Expr(:call, :∨, children...) => interpret( - SyntaxBranch(∨, [interpret(ch, i, args...; kwargs...) for ch in children]...), - i, - args...; - kwargs..., - ) - Expr(:call, :∧, children...) => interpret( - SyntaxBranch(∧, [interpret(ch, i, args...; kwargs...) for ch in children]...), - i, - args...; - kwargs..., - ) - Expr(:call, :¬, child) => interpret( - SyntaxBranch(¬, interpret(child, i, args...; kwargs...)), - i, - args...; - kwargs..., - ) - _ => error("missing a match for $φ") - end - - return interpret(syntax_branch, i, args...; kwargs...) -end - -function interpret_qn( - e::Union{AbstractString,Integer,Expr,Atom}, - qn_state::AbstractVector{<:Integer}, - vertex_names::AbstractVector{<:AbstractString}, -) - state_map = Dict(zip(vertex_names, deepcopy(qn_state))) - - _int(e) = @match e begin - ::AbstractString => state_map[e] - ::Atom => state_map[e] - ::Integer => e - :($v1 + $v2) => _int(v1) + _int(v2) - :($v1 - $v2) => _int(v1) - _int(v2) - :($v1 / $v2) => _int(v1) / _int(v2) - :($v1 * $v2) => _int(v1) * _int(v2) - :(Min($v1, $v2)) => min(_int(v1), _int(v2)) - :(Max($v1, $v2)) => max(_int(v1), _int(v2)) - :(Ceil($v)) => ceil(_int(v)) - :(Floor($v)) => floor(_int(v)) - _ => error("Unhandled Expr in `interpret`: $e, $(typeof(e))") - end - - return _int(e) -end - -function evaluate_bn(problem::UndirectedProblem, expr, vertex_names) - sat_examples = BitVector[] - - function _eval_1_dir(in, out) - truth = TruthDict(Dict(zip(vertex_names, in[:state]))) - res = interpret(expr, truth) - expected = BooleanTruth(out[:state][findfirst(==(problem.name), vertex_names)]) - success = expected == res - - return success - end - - for example ∈ problem.examples - success_direction1 = _eval_1_dir(example.data1, example.data2) - success_direction2 = _eval_1_dir(example.data2, example.data1) - - success = BitVector([success_direction1, success_direction2]) - - push!(sat_examples, success) - end - - return sat_examples -end - -function evaluate_qn(problem::UndirectedProblem, expr, vertex_names) - sat_examples = BitVector[] - - function _eval_1_dir(in, out) - res = interpret_qn(expr, in[:state], vertex_names) - expected = out[:state][findfirst(==(problem.name), vertex_names)] - success = expected == res - return success - end - - for example ∈ problem.examples - success_direction1 = _eval_1_dir(example.data1, example.data2) - success_direction2 = _eval_1_dir(example.data2, example.data1) - - success = BitVector([success_direction1, success_direction2]) - - push!(sat_examples, success) - end - - return sat_examples -end diff --git a/experiments/Synth/src/gather_bn_data.jl b/experiments/Synth/src/gather_bn_data.jl deleted file mode 100644 index a0e1cb2..0000000 --- a/experiments/Synth/src/gather_bn_data.jl +++ /dev/null @@ -1,71 +0,0 @@ -using GraphDynamicalSystems.BooleanNetworks: sample_boolean_network, abn -using DynamicalSystems: trajectory, ArbitrarySteppable, length, StateSpaceSet - -function gather_bn_data(bn::ArbitrarySteppable, iterations::Int) - return trajectory(bn, iterations)[1] -end - -function split_state_space(trajectory::StateSpaceSet) - # split into pairs of input (all values) and output (changed value) - # - input_output_pairs_per_node = Dict{Int,Set{Tuple{Vector{Int},Vector{Int}}}}() - for i = 1:length(trajectory)-1 - changed = findfirst(trajectory[i+1] .!= trajectory[i]) - # only proceed if there was a change - if !isnothing(changed) - - # in real data we don't know the direction of the transition - # was it from i -> i+1 or i+1 -> i, we only know that two - # states are adjacent, so for synthesis, we want to add the pair - # of i and i+1, and test possible programs on both. - - existing_pairs = get( - input_output_pairs_per_node, - changed, - Set{Tuple{Vector{Int},Vector{Int}}}(), - ) - push!(existing_pairs, (trajectory[i], trajectory[i+1])) - input_output_pairs_per_node[changed] = existing_pairs - end - end - - return input_output_pairs_per_node -end - -function get_split_state_space( - network_size::Int, - max_equation_depth::Int, - iterations::Int, - seed::Int, -) - bn = sample_boolean_network(network_size, max_equation_depth, seed) - async_bn = abn(bn; seed = seed) - trajectory = gather_bn_data(async_bn, iterations) - return split_state_space(trajectory), bn, async_bn, trajectory -end - -function get_split_state_space(params::Dict{String,<:Any}) - @unpack network_size, max_equation_depth, iterations, repetitions, seed = params - - save_data::Dict{String,Any} = copy(params) - - all_divided_state_space = Dict{Int,Set{Tuple{<:AbstractVector{Int},Int}}}() - bn = sample_boolean_network(network_size, max_equation_depth, seed) - all_abns = [abn(bn; seed = seed * r) for r = 1:repetitions] - - for a in all_abns - trajectory = gather_bn_data(a, iterations) - divided_state_space = split_state_space(trajectory) - for (node, pairs) in divided_state_space - existing_pairs = get(all_divided_state_space, node, Set()) - push!(existing_pairs, pairs...) - all_divided_state_space[node] = existing_pairs - end - end - - save_data["specifications"] = all_divided_state_space - save_data["bn"] = bn - save_data["abns"] = all_abns - - return save_data -end diff --git a/experiments/Synth/src/grammars.jl b/experiments/Synth/src/grammars.jl deleted file mode 100644 index 7d5d714..0000000 --- a/experiments/Synth/src/grammars.jl +++ /dev/null @@ -1,76 +0,0 @@ -using AbstractTrees: Leaves -using AbstractTrees -using HerbConstraints: StateHole, freeze_state -using HerbCore: get_rule - -conjunctive = @cfgrammar begin - Start = CNF - CNF = Disj ∧ CNF - CNF = Disj - Disj = Lit ∨ Disj - Disj = Lit - Lit = Var - Lit = ¬Var -end - -disjunctive = @cfgrammar begin - Start = DNF - DNF = Conj ∨ DNF - DNF = Conj - Conj = Lit ∧ Conj - Conj = Lit - Lit = Var - Lit = ¬Var -end - -function _add_variables(grammar, vars::AbstractVector{<:Union{<:AbstractString,<:Atom}}) - for i in vars - add_rule!(grammar, :(Var = Atom($i))) - end -end - -function _add_variables(grammar, vars::AbstractVector{<:Integer}) - for i in vars - add_rule!(grammar, :(Var = Atom($(string(i))))) - end -end - -_add_variables(grammar, num_vars::Int) = - _add_variables(grammar, string.(collect(1:num_vars))) - -function build_cnf_grammar(num_vars::Int) - grammar = deepcopy(conjunctive) - _add_variables(grammar, num_vars) - - return grammar -end - -function build_cnf_grammar(vars::AbstractVector) - grammar = deepcopy(conjunctive) - _add_variables(grammar, vars) - - return grammar -end - -function build_dnf_grammar(num_vars::Int) - grammar = deepcopy(disjunctive) - _add_variables(grammar, num_vars) - - return grammar -end - -function build_dnf_grammar(vars::AbstractVector) - grammar = deepcopy(disjunctive) - _add_variables(grammar, vars) - - return grammar -end - -function count_neighbors_in_expr(r::AbstractRuleNode, grammar::ContextSensitiveGrammar) - leaves_in_expr = Set(map(x -> get_rule(x), Leaves(r))) - terminal_indices = findall(grammar.isterminal) - - return count(x -> x in terminal_indices, leaves_in_expr) -end - -AbstractTrees.children(node::StateHole) = node.children diff --git a/experiments/Synth/src/parse_aeon.jl b/experiments/Synth/src/parse_aeon.jl deleted file mode 100644 index 3cc1efa..0000000 --- a/experiments/Synth/src/parse_aeon.jl +++ /dev/null @@ -1,102 +0,0 @@ -module AEONParser -using ParserCombinator: - Equal, Pattern, Drop, parse_one, parse_dbg, Trace, @with_names, set_name - -using SoleLogics: parseformula, Atom, ∨, ∧, ¬, AbstractSyntaxStructure - -using Term: Progress, ProgressBar - -# See the following for source: -# https://biodivine.fi.muni.cz/aeon/manual/v0.4.0/model_editor/import_export.html#aeon-format - -# > Since SBML is quite hard to edit by hand, as well as parse correctly, -# > we also provide a simplified text based format. In this format, the -# > regulatory graph is described as a list of edges, where each edge is -# > encoded as regulator [->,-|,-?,->?,-|?,-??] target. Here, regulator and -# > target are the names of the network variables, and the arrow connecting -# > them describes the type of regulation. Then -> denotes activation, -| -# > inhibition and -? is unspecified monotonicity. Finally, an extra ? signifies -# > that the regulation is non-observable. -# > -# > An update function for variable X is written as $A: function, where the format -# > of the actual functions in .aeon files is the same as in the edit fields in the -# > AEON interface. Additional information (model name, description and layout) is -# > encoded in comments (lines starting with #). The order of declarations is not -# > taken into account. -# > [...] - -struct NetworkVar - name::Any -end - -@enum RegulationType begin - activation - inhibition - unspecified -end - -get_reg_type = - reg_str -> Dict([">" => activation, "|" => inhibition, "?" => unspecified])[reg_str] - -struct Regulation - regulator::NetworkVar - reg_type::RegulationType - is_observable::Bool - target::NetworkVar -end - -struct UpdateFunction - target::NetworkVar - fn::AbstractSyntaxStructure -end - -Regulation(reg::NetworkVar, reg_type::RegulationType, target::NetworkVar) = - Regulation(reg, reg_type, true, target) - -""" - aeon2sole(formula::AbstractString) - -Replaces the connectives in an AEON-style formula (!(A | B) & C) -with a SoleLogics.jl-style formula (¬(A ∨ B) ∧ C). -""" -function aeon2sole(formula::AbstractString) - return replace(formula, "&" => "∧", "|" => "∨", "!" => "¬") -end - -@with_names begin - # Matches "word" characters, ex: v_IL1R1, v_IRF2 - network_var = Pattern("\\w+") > NetworkVar - # Matches 0 or more whitespace characters and ignores (drops) them - spaces = Drop(Pattern("\\s")[0:end]) - # Matches the '-' character - edge = Drop(Equal("-")) - # Matches '>' or '|' or '?' and maps it to an activation type - reg_type = (Equal(">") | Equal("|") | Equal("?")) > get_reg_type - # Matches 0 or 1 '?' characters, then maps to false if 1, true if 0 - is_observable = Equal("?")[0:1] |> isempty - - regulation = spaces + edge + reg_type + is_observable + spaces - - regulation_line = (network_var + regulation + network_var) > Regulation - - fn_line_begin = Drop(Equal("\$")) - - fn_line = - fn_line_begin + - network_var + - Drop(Equal(":")) + - spaces + - # Convert the rest into a SoleLogics function - ((Pattern(".")[0:end] |> join > aeon2sole) > parseformula) > UpdateFunction - - each_line = (fn_line | regulation_line) -end - -function parse_aeon_file(filepath::AbstractString) - model = [] - for l in readlines(filepath) - push!(model, parse_one(l, each_line)...) - end - return model -end -end diff --git a/experiments/Synth/src/synth_process.jl b/experiments/Synth/src/synth_process.jl deleted file mode 100644 index e6921c0..0000000 --- a/experiments/Synth/src/synth_process.jl +++ /dev/null @@ -1,133 +0,0 @@ -using Graphs: nv -using Attractors: AttractorsViaRecurrences, basins_of_attraction -using DynamicalSystems: set_distance -using Random: seed! - -function synth_biodivine( - problem, - iterator, - grammar, - max_iterations, - evaluator, - vertex_names, -) - exprs_and_scores = [] - - for (i, ex) in enumerate(iterator) - if i % 100000 == 0 - @info "$i iterations, problem $(problem.name)" - end - - expr = rulenode2expr(ex, grammar) - - sat_examples = nothing - try - sat_examples = evaluator(problem, expr, vertex_names) - catch e - @error "Problem evaluating: Problem name $(problem.name), expr: $expr, i: $i." - rethrow(e) - end - - if isnothing(sat_examples) - push!(exprs_and_scores, (expr, nothing, nothing, i)) - # if all examples worked in at least one direction - elseif sum(all.(==(false), sat_examples)) == 0 - score = sum(count.(sat_examples)) / (2 * length(problem.examples)) - push!(exprs_and_scores, (expr, score, sat_examples, i)) - end - - if i > max_iterations - @warn "Maximum iterations reached" - break - end - end - - return exprs_and_scores -end - -rules(g) = g.rules -types(g) = g.types - -function get_basins_qn(model) - async_qn = aqn(model, 1) - - grid = Tuple(range(0, 1) for _ = 1:nv(model)) - - mapper = AttractorsViaRecurrences(async_qn, grid; consecutive_recurrences = 1000) - - basins = basins_of_attraction(mapper, grid) - - return basins -end - -function get_basins_bn(model) - async_bn = abn(model) - - grid = Tuple(range(0, 1) for _ = 1:nv(model)) - - mapper = AttractorsViaRecurrences(async_bn, grid; consecutive_recurrences = 1000) - - basins = basins_of_attraction(mapper, grid) - - return basins -end - -function synth_complete_qn(df, id, target_basins; max_iterations = 1000) - seed!(37) - - df = df[df.id.==id, :] - @assert allequal(rules.(df.grammar)) && allequal(types.(df.grammar)) - grammar = first(df.grammar) - - function _get_resulting_basins(sol_assignments) - - selected_functions = getindex.(getindex.(df.exprs_and_scores, sol_assignments), 1) - entity_names_to_functions = Dict(Symbol.(df.vertex) .=> selected_functions) - - qn = GraphDynamicalSystems.update_functions_to_network( - entity_names_to_functions, - grammar, - ) - - resulting_basins = get_basins_qn(qn) - - return resulting_basins - end - - sol_assignments = ones(Int, nrow(df)) - - resulting_basins = _get_resulting_basins(sol_assignments) - - best_distance = - set_distance.(zip(values(resulting_basins[2]), values(target_basins[2]))...) - - i = 0 - - while values(target_basins[2]) ∉ values(resulting_basins[2]) && i < max_iterations - i += 1 - if i % 1000 == 0 - @info "$i iterations, best distance: $best_distance, assigments: $sol_assignments" - end - - for sol_to_bump in shuffle(eachindex(sol_assignments)) - if length(resulting_basins[2]) == length(target_basins[2]) - current_distance = [ - set_distance(x1, x2) for (x1, x2) in - zip(values(resulting_basins[2]), values(target_basins[2])) - ] - if all(current_distance .<= best_distance) - @info "new best distance: $current_distance\nassigments: $sol_assignments\n$(getindex.(getindex.(df.exprs_and_scores, sol_assignments), 1))\n" - best_distance = current_distance - end - end - - sol_assignments[sol_to_bump] = min( - sol_assignments[sol_to_bump] + 1, - length(df.exprs_and_scores[sol_to_bump]), - ) - resulting_basins = _get_resulting_basins(sol_assignments) - end - end - - return resulting_basins, sol_assignments -end diff --git a/experiments/Synth/src/undirected_specification.jl b/experiments/Synth/src/undirected_specification.jl deleted file mode 100644 index 6b64133..0000000 --- a/experiments/Synth/src/undirected_specification.jl +++ /dev/null @@ -1,28 +0,0 @@ -using HerbSpecification -using DocStringExtensions - -""" - $(TYPEDEF) - -Specification where it is not known whether f(data1) == data2 or f(data2) == data1. - -$(TYPEDFIELDS) -""" -struct UndirectedExample - "Either the input or output for this example." - data1::Dict{Symbol,Any} - "If data1 is the input, this field is the output, and vice-versa." - data2::Dict{Symbol,Any} -end - -""" - $(TYPEDEF) - -Problem defined over `UndirectedExample`s. - -$(TYPEDFIELDS) -""" -struct UndirectedProblem - name::AbstractString - examples::AbstractVector{UndirectedExample} -end diff --git a/experiments/Synth/test/001.aeon b/experiments/Synth/test/001.aeon deleted file mode 100644 index 7c9077b..0000000 --- a/experiments/Synth/test/001.aeon +++ /dev/null @@ -1,835 +0,0 @@ -v_IL1R1 -> v_ILIB_IL1R1 -v_ILIB -> v_ILIB_IL1R1 -v_STAT1_STAT1nucleus_p2 -> v_ICAM1gene -v_External_Activator -> v_FAF1 -v_CD40_CD40LG -> v_CD40_CD40LG_TRAF3 -v_TRAF3 -> v_CD40_CD40LG_TRAF3 -v_CASP7cytoplasm -> v_CASP7nucleus -v_NFKB2p100_RELBcytoplasm -> v_NFKB2p100_RELB_Ub -v_Ub -> v_NFKB2p100_RELB_Ub -v_TNFRSF1B -> v_TNF_TNFRSF1B -v_TNF -> v_TNF_TNFRSF1B -v_External_Activator -> v_IKBKG -v_External_Activator -> v_APAF1 -v_External_Activator -> v_HSPA1A -v_External_Activator -> v_IL1R1 -v_IRF1_IRF1nucleus -> v_EIF2AK2gene -v_ISGF3nucleus -> v_EIF2AK2gene -v_External_Activator -> v_CHUK -v_IRF5_IRF5nucleus -> v_RIPK1gene -v_EIF2S1 -> v_Inactivation_of_Protein_Synthesis -v_External_Activator -> v_PTP -v_ENDOGcytoplasm -> v_ENDOGnucleus -v_External_Activator -> v_RELB -v_External_Activator -> v_TNFRSF10A -v_IFNGR -> v_STAT1 -v_PDCD8cytoplasm -> v_PDCD8_HSPA1A -v_HSPA1A -> v_PDCD8_HSPA1A -v_TNF_TNFRSF1B -> v_TNF_TNFRSF1B_TRAF2 -v_TRAF2cytoplasm -> v_TNF_TNFRSF1B_TRAF2 -v_IRF7 -> v_IRF7_IRF7cytoplasm -v_IRAK1 -> v_ILIB_IL1R1_MYD88_IRAK1_IRAK4 -v_MYD88 -> v_ILIB_IL1R1_MYD88_IRAK1_IRAK4 -v_ILIB_IL1R1 -> v_ILIB_IL1R1_MYD88_IRAK1_IRAK4 -v_IRAK4 -> v_ILIB_IL1R1_MYD88_IRAK1_IRAK4 -v_IRF1_IRF1nucleus -> v_OAS1gene -v_ISGF3nucleus -> v_OAS1gene -v_IRF8 -> v_OAS1gene -v_IRF5_IRF5nucleus -> v_OAS1gene -v_IRF2_Activator -> v_IRF2 -v_ISGF3cytoplasm -> v_ISGF3nucleus -v_External_Activator -> v_TYK2 -v_TNF_TNFRSF1A_FADD_TRADD -> v_TNF_TNFRSF1A_FADD_TRADD_TRAF2_RIPK1 -v_RIPK1 -> v_TNF_TNFRSF1A_FADD_TRADD_TRAF2_RIPK1 -v_TRAF2cytoplasm -> v_TNF_TNFRSF1A_FADD_TRADD_TRAF2_RIPK1 -v_IRF1_IRF1nucleus -> v_HIST2H4gene -v_IRF2 -> v_HIST2H4gene -v_TNFRSF17 -> v_TNFSF13B_TNFRSF17 -v_TNFSF13B -> v_TNFSF13B_TNFRSF17 -v_External_Activator -> v_CFLAR -v_BAG4_TNFRSF1A -> v_TNF_BAG4_TNFRSF1A -v_TNF -> v_TNF_BAG4_TNFRSF1A -v_DNA_Fragmentation -> v_Apoptosis -v_Inactive_DNA_Repair -> v_Apoptosis -v_Inactivation_of_Protein_Synthesis -> v_Apoptosis -v_Cell_Shrinkage -> v_Apoptosis -v_TLR9_TLR9 -> v_TLR9_MYD88_IRAK1_IRAK4 -v_IRAK1 -> v_TLR9_MYD88_IRAK1_IRAK4 -v_MYD88 -> v_TLR9_MYD88_IRAK1_IRAK4 -v_IRAK4 -> v_TLR9_MYD88_IRAK1_IRAK4 -v_IRF5_IRF5nucleus -> v_PMAIPgene -v_Triacyl_Lipopeptides -> v_TLR1_TLR2 -v_External_Activator -> v_PRKCZ -v_TRAF6 -> v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4_TRAF6 -v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4 -> v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4_TRAF6 -v_External_Activator -> v_IFNAR2 -v_IRF3_IRF3cytoplasm -> v_IRF3_IRF3nucleus -v_STAT1_STAT1nucleus_p2 -> v_CXCL9gene -v_IRF1_IRF1nucleus -> v_STAT1gene -v_IRF5_IRF5nucleus -> v_BAK1gene -v_External_Activator -> v_TICAM2 -v_IRF3_IRF7nucleus -> v_IFNAgene -v_IRF3_IRF3nucleus -> v_IFNAgene -v_IRF3_IRF5nucleus -> v_IFNAgene -v_IRF1_IRF1nucleus -> v_IFNAgene -v_STAT1_STAT1_IRF9nucleus -> v_IFNAgene -v_IRF2 -|? v_IFNAgene -v_ISGF3nucleus -> v_IFNAgene -v_IRF5_IRF5nucleus -> v_IFNAgene -v_IRF7_IRF7nucleus -> v_IFNAgene -v_IRF8 -> v_G1P3gene -v_TBK1 -> v_RELAp65_NFKB1p50cytoplasm -v_CHUK -> v_RELAp65_NFKB1p50cytoplasm -v_IKBKB -> v_RELAp65_NFKB1p50cytoplasm -v_NFKBIA_RELAp65_NFKB1p50 -> v_RELAp65_NFKB1p50cytoplasm -v_MAP3K7 -> v_RELAp65_NFKB1p50cytoplasm -v_RPS6KA5 -> v_RELAp65_NFKB1p50cytoplasm -v_PRKCZ -> v_RELAp65_NFKB1p50cytoplasm -v_CASP6cytoplasm -> v_CASP6nucleus -v_TBK1 -> v_TLR3_TICAM1_TICAM2_TBK1 -v_TLR3_TICAM1_TICAM2 -> v_TLR3_TICAM1_TICAM2_TBK1 -v_PDCD8cytoplasm -> v_PDCD8nucleus -v_External_Activator -> v_ProCASP8 -v_TLR9_MYD88_IRAK1_IRAK4 -> v_TLR9_MYD88_IRAK1_IRAK4_TRAF6 -v_TRAF6 -> v_TLR9_MYD88_IRAK1_IRAK4_TRAF6 -v_NFKB2p52_RELBcytoplasm -> v_NFKB2p52_RELBnucleus -v_TRAF6 -> v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4_TRAF6 -v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4 -> v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4_TRAF6 -v_IRF5 -> v_IRF5_IRF5cytoplasm -v_External_Activator -> v_GAS2 -v_External_Activator -> v_TIRAP -v_CASP6_Activator -> v_CASP9 -v_NFKB2p52_NFKB2p52nucleus -> v_BCL3_NFKB2p52_NFKB2p52 -v_BCL3 -> v_BCL3_NFKB2p52_NFKB2p52 -v_IPARP -> v_Inactive_DNA_Repair -v_TLR9_MYD88_IRAK1_IRAK4_TRAF6 -> v_TLR9_MYD88_TRAF6 -v_IRF5 -> v_IRF5_IRF7cytoplasm -v_External_Activator -> v_IRAK4 -v_RELAp65_NFBK1p50nucleus -> v_BCL2L1gene -v_Bacteria -> v_LPS -v_Mitochondrial_Activator -> v_BCL2 -v_ProCASP10 -> v_CASP10 -v_TNF_TNFRSF1A_FADD_TRADD -> v_CASP10 -v_dsRNA -> v_EIF2AK2cytoplasm -v_External_Activator -> v_LMNA -v_cLMNA -> v_Cell_Shrinkage -v_cGAS2 -> v_Cell_Shrinkage -v_External_Activator -> v_TLR6 -v_Bacteria -> v_Flagellin -v_IRF1_IRF1nucleus -> v_IL12Bgene -v_CASP8 -> v_tBID -v_BID -> v_tBID -v_CASP2 -> v_tBID -v_IKBKE -> v_IKBKE_TBK1 -v_TBK1 -> v_IKBKE_TBK1 -v_TLR4_TICAM1_TICAM2 -> v_IKBKE_TBK1 -v_CHUK -> v_CHUK_CHUK -v_MAP3K14 -> v_CHUK_CHUK -v_IRF9 -> v_STAT1_STAT1_IRF9cytoplasm -v_STAT1 -> v_STAT1_STAT1_IRF9cytoplasm -v_APAF1_CYCS -> v_Apoptosome -v_CASP9 -> v_Apoptosome -v_External_Activator -> v_PRKCD -v_TLR3_TICAM1_TICAM2 -> v_TLR3_TICAM1_TICAM2_TRAF6 -v_TRAF6 -> v_TLR3_TICAM1_TICAM2_TRAF6 -v_IRF7_IRF7cytoplasm -> v_IRF7_IRF7nucleus -v_TLR1_TLR2 -> v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4 -v_IRAK2 -> v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4 -v_TIRAP -> v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4 -v_IRAK1 -> v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4 -v_MYD88 -> v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4 -v_IRAK4 -> v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4 -v_CASP6_Activator -> v_ProCASP6 -v_TLR5 -> v_TLR5_TLR5 -v_Flagellin -> v_TLR5_TLR5 -v_External_Activator -> v_NOS2Agene -v_IRF2 -|? v_NOS2Agene -v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4_TRAF6 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_TLR9_MYD88_IRAK1_IRAK4_TRAF6 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_MAP3K7IP1 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_TLR3_TICAM1_TICAM2_TRAF6 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_TLR7_MYD88_IRAK1_IRAK4_TRAF6 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_MAP3K7IP2 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4_TRAF6 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_TLR5_MYD88_IRAK1_IRAK4_TRAF6 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_MAP3K7 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_ILIB_IL1R1_MYD88_IRAK1_IRAK4_TRAF6 -> v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -v_External_Activator -> v_TICAM1 -v_Bacteria -> v_Viral_Bacterial_CpG -v_Virus -> v_Viral_Bacterial_CpG -v_RELAp65_NFBK1p50nucleus -> v_BCL2A1 -v_External_Activator -> v_ProCASP2 -v_TLR6 -> v_TLR2_TLR6 -v_Diacyl_Lipopeptides -> v_TLR2_TLR6 -v_TLR2 -> v_TLR2_TLR6 -v_External_Activator -> v_JAK1 -v_External_Activator -> v_ProCASP1 -v_External_Activator -> v_Proteasome -v_PRKCD -> v_STAT1_STAT1cytoplasm -v_STAT1 -> v_STAT1_STAT1cytoplasm -v_GAS2 -> v_cGAS2 -v_CASP3nucleus -> v_cGAS2 -v_External_Activator -> v_RIPK3 -v_TLR3_TICAM1_TICAM2_RIPK1 -> v_TLR3_TICAM1_TICAM2_RIPK1_RIPK3 -v_RIPK3 -> v_TLR3_TICAM1_TICAM2_RIPK1_RIPK3 -v_External_Activator -> v_MAP3K7IP2 -v_Virus -> v_dsRNA -v_Proteasome -> v_NFKB2p52_NFKB2p52cytoplasm -v_NFKB2p100_NFKB2p100_Ub -> v_NFKB2p52_NFKB2p52cytoplasm -v_tBID -> v_PDCD8cytoplasm -v_PDCD8mitochondria -> v_PDCD8cytoplasm -v_TP53nucleus -> v_BBC3gene -v_IRF5_IRF5nucleus -> v_PLSCR1gene -v_RELAp65_NFBK1p50nucleus -> v_BCL2A1gene -v_IRF1_IRF1nucleus -> v_GBP1gene -v_IRF2 -> v_GBP1gene -v_IRF9 -> v_ISGF3cytoplasm -v_STAT1_STAT2 -> v_ISGF3cytoplasm -v_IRF1_IRF1cytoplasm -> v_IRF1_IRF1nucleus -v_CASP6nucleus -> v_cLMNA -v_LMNA -> v_cLMNA -v_TRAF6 -> v_TLR7_MYD88_IRAK1_IRAK4_TRAF6 -v_TLR7_MYD88_IRAK1_IRAK4 -> v_TLR7_MYD88_IRAK1_IRAK4_TRAF6 -v_CASP8 -> v_CASP7cytoplasm -v_ProCASP7 -> v_CASP7cytoplasm -v_BIRC4cytoplasm -|? v_CASP7cytoplasm -v_Apoptosome -> v_CASP7cytoplasm -v_CASP10 -> v_CASP7cytoplasm -v_SPI1 -> v_CYBBgene -v_IRF8 -> v_CYBBgene -v_External_Activator -> v_TBK1 -v_External_Activator -> v_TNFRSF10B -v_TP53cytoplasm -> v_TP53nucleus -v_IRF3_IRF7nucleus -> v_G1P2gene -v_IRF3_IRF3nucleus -> v_G1P2gene -v_IRF1_IRF1nucleus -> v_G1P2gene -v_SPI1 -> v_G1P2gene -v_IRF2 -> v_G1P2gene -v_IRF4 -> v_G1P2gene -v_ISGF3nucleus -> v_G1P2gene -v_CBP -> v_G1P2gene -v_IRF8 -> v_G1P2gene -v_TNF_BAG4_TNFRSF1A -> v_TNF_TNFRSF1A -v_External_Activator -> v_JAK2 -v_FASLG_FAS_FADD_FAF1_DAXX -> v_FASLG_FAS_FADD_FAF1_DAXX_CFLAR -v_CFLAR -> v_FASLG_FAS_FADD_FAF1_DAXX_CFLAR -v_IKBKE_TBK1 -> v_IRF7 -v_TP53nucleus -> v_APAF1gene -v_External_Activator -> v_Ub -v_IRF5_IRF5nucleus -> v_PRKRAgene -v_IRF5_IRF5nucleus -> v_CXCL11gene -v_IFNAR -> v_STAT2 -v_IRF3_IRF7nucleus -> v_IFIT2gene -v_IRF3_IRF3nucleus -> v_IFIT2gene -v_ISGF3nucleus -> v_IFIT2gene -v_CBP -> v_IFIT2gene -v_TNFSF10 -> v_TNFSF10_TNFSF10B -v_TNFRSF10B -> v_TNFSF10_TNFSF10B -v_RELA_NFKB1_Activator -> v_RELA_p65 -v_External_Activator -> v_IFNGR1 -v_Mitochondrial_Activator -> v_BAK1 -v_External_Activator -> v_TOLLIP -v_IFNGR2 -> v_IFNGR -v_JAK2 -> v_IFNGR -v_SOCS3 -|? v_IFNGR -v_SOCS1 -|? v_IFNGR -v_IFNG -> v_IFNGR -v_IFNGR1 -> v_IFNGR -v_PTPN2 -|? v_IFNGR -v_NFKB2p52_NFKB2p52cytoplasm -> v_NFKB2p52_NFKB2p52nucleus -v_FAS -> v_FASLG_FAS -v_FASLG -> v_FASLG_FAS -v_External_Activator -> v_TNFRSF1A -v_RELAp65_NFBK1p50nucleus -> v_BIRC4gene -v_Mitochondrial_Activator -> v_BAD -v_RELAp65_NFBK1p50nucleus -> v_CFLARgene -v_External_Activator -> v_IKBKB -v_TOLLIP -> v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4 -v_IRAK1 -> v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4 -v_MYD88 -> v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4 -v_TLR2_TLR6 -> v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4 -v_IRAK4 -> v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4 -v_TRAF6 -> v_TLR7_MYD88_TRAF6_IRF5 -v_TLR7_TLR7 -> v_TLR7_MYD88_TRAF6_IRF5 -v_MYD88 -> v_TLR7_MYD88_TRAF6_IRF5 -v_IRF5 -| v_TLR7_MYD88_TRAF6_IRF5 -v_BAG4 -> v_BAG4_TNFRSF1A -v_TNFRSF1A -> v_BAG4_TNFRSF1A -v_External_Activator -> v_TNFRSF1B -v_CASP9 -> v_CASP6cytoplasm -v_ProCASP6 -> v_CASP6cytoplasm -v_TLR4 -> v_TLR4_TICAM1_TICAM2 -v_TICAM2 -> v_TLR4_TICAM1_TICAM2 -v_TICAM1 -> v_TLR4_TICAM1_TICAM2 -v_Mitochondrial_Activator -> v_CYCSmytochondria -v_External_Activator -> v_TLR5 -v_RELAp65_NFBK1p50nucleus -> v_TRAF2gene -v_IRF5_IRF7cytoplasm -> v_IRF5_IRF7nucleus -v_TYK2 -> v_IFNAR -v_IFNA -> v_IFNAR -v_IFNAR1 -> v_IFNAR -v_IFNB1 -> v_IFNAR -v_IFNAR2 -> v_IFNAR -v_JAK1 -> v_IFNAR -v_External_Activator -> v_TRAF5 -v_External_Activator -> v_PRKRA -v_CREBBP -> v_CBP -v_EP300 -> v_CBP -v_RELAp65_NFBK1p50nucleus -> v_FASgene -v_IRF1_IRF1nucleus -> v_CIITAgene -v_IRF2 -> v_CIITAgene -v_IRF5 -> v_IRF3_IRF5cytoplasm -v_IRF1_IRF1nucleus -> v_TAP1gene -v_IRF2 -> v_TAP1gene -v_EIF2AK2_PRKRA -> v_TP53cytoplasm -v_External_Activator -> v_SOCS3 -v_External_Activator -> v_FAS -v_External_Activator -> v_ATF2 -v_FADD -> v_TNFSF10_TNFRSF10A_FADD -v_TNFSF10_TNFRSF10A -> v_TNFSF10_TNFRSF10A_FADD -v_External_Activator -> v_TLR7 -v_External_Activator -> v_TLR9 -v_IRF1_IRF1nucleus -> v_IL15gene -v_Virus -> v_ssRNA -v_External_Activator -> v_TLR2 -v_tBID -> v_ENDOGcytoplasm -v_ENDOGmitochondria -> v_ENDOGcytoplasm -v_External_Activator -> v_BIRC2 -v_ProCASP4 -> v_CASP4 -v_DNA_Fragmentation -> v_Fragmented_DNAcytoplasm -v_IRF1cytoplasm -> v_IRF1_IRF1cytoplasm -v_BCL2L1mitochondria -> v_BCL2L1_BAD -v_BAD -> v_BCL2L1_BAD -v_External_Activator -> v_CD40 -v_External_Activator -> v_RIPK1 -v_IRF3_IRF7nucleus ->? v_IFNB1gene -v_IRF3_IRF3nucleus -> v_IFNB1gene -v_RELAp65_NFBK1p50nucleus ->? v_IFNB1gene -v_STAT1_STAT1nucleus_p2 -> v_IFNB1gene -v_IRF1_IRF1nucleus -> v_IFNB1gene -v_IRF2 -|? v_IFNB1gene -v_ISGF3nucleus -> v_IFNB1gene -v_ATF2 ->? v_IFNB1gene -v_CBP ->? v_IFNB1gene -v_IRF5_IRF5nucleus -> v_IFNB1gene -v_ProCASP2 -> v_CASP2 -v_Fragmented_DNAcytoplasm -> v_CASP2 -v_External_Activator -> v_BID -v_IRF3_IRF7nucleus -> v_CXCL10gene -v_IRF3_IRF3nucleus -> v_CXCL10gene -v_ISGF3nucleus -> v_CXCL10gene -v_CBP -> v_CXCL10gene -v_DFFA -> v_IDFFA -v_CASP3nucleus -> v_IDFFA -v_IRF3 -> v_IRF3_IRF7cytoplasm -v_IRF7 -> v_IRF3_IRF7cytoplasm -v_TP53nucleus -> v_MDM2gene -v_External_Activator -> v_CREBBP -v_STAT2 -> v_STAT1_STAT2 -v_STAT1 -> v_STAT1_STAT2 -v_CASP7_Activator -> v_ProCASP7 -v_STAT1_STAT1nucleus_p1 -> v_STAT1_STAT1nucleus_p2 -v_PTP -|? v_STAT1_STAT1nucleus_p2 -v_TRAF6 -> v_TLR9_MYD88_TRAF6_IRF5 -v_TLR9_TLR9 -> v_TLR9_MYD88_TRAF6_IRF5 -v_MYD88 -> v_TLR9_MYD88_TRAF6_IRF5 -v_IRF5 -| v_TLR9_MYD88_TRAF6_IRF5 -v_TNF_IKBKG_Complex -> v_NFKBIA_RELAp65_NFKB1p50 -v_NFKBIA -> v_NFKBIA_RELAp65_NFKB1p50 -v_IKBKG_CHUK_IKBKB -> v_NFKBIA_RELAp65_NFKB1p50 -v_RELA_p65 -> v_NFKBIA_RELAp65_NFKB1p50 -v_NFKB1_p50 -> v_NFKBIA_RELAp65_NFKB1p50 -v_RELA_NFKB1_Activator -> v_NFKB1_p50 -v_RELAp65_NFKB1p50cytoplasm -> v_RELAp65_NFBK1p50nucleus -v_IRF1_IRF1_Activator -> v_CKII -v_DIABLOcytoplasm -| v_BIRC4cytoplasm -v_HTRA2cytoplasm -| v_BIRC4cytoplasm -v_External_Activator -> v_IRAK2 -v_External_Activator -> v_TRAF3 -v_IRF1_IRF1nucleus -> v_TNFSF10gene -v_IRF5_IRF5nucleus -> v_TNFSF10gene -v_External_Activator -> v_ProCASP4 -v_External_Activator -> v_DAXX -v_BCL2 -> v_BCL2_BAX -v_BAX -> v_BCL2_BAX -v_PKC -> v_IRF1cytoplasm -v_CKII -> v_IRF1cytoplasm -v_PKA -> v_IRF1cytoplasm -v_External_Activator -> v_SPI1 -v_PARP -> v_IPARP -v_CASP7nucleus -> v_IPARP -v_CASP3nucleus -> v_IPARP -v_External_Activator -> v_TNFRSF17 -v_TLR3_TICAM1_TICAM2 -> v_TLR3_TICAM1_TICAM2_RIPK1 -v_RIPK1 -> v_TLR3_TICAM1_TICAM2_RIPK1 -v_SPI1 -> v_IL1Bgene -v_IRF8 -> v_IL1Bgene -v_STAT1_STAT1_IRF9cytoplasm -> v_STAT1_STAT1_IRF9nucleus -v_External_Activator -> v_DFFA -v_BAK1 -> v_CYCScytoplasm -v_tBID -> v_CYCScytoplasm -v_BAX -> v_CYCScytoplasm -v_CYCSmytochondria -> v_CYCScytoplasm -v_External_Activator -> v_TRADD -v_CASP3_Activator -> v_ProCASP3 -v_TLR5_TLR5 -> v_TLR5_MYD88_IRAK1_IRAK4 -v_IRAK1 -> v_TLR5_MYD88_IRAK1_IRAK4 -v_MYD88 -> v_TLR5_MYD88_IRAK1_IRAK4 -v_IRAK4 -> v_TLR5_MYD88_IRAK1_IRAK4 -v_TNFSF10_TNFSF10B_FADD -> v_CASP8 -v_FASLG_FAS_FADD_FAF1_DAXX -> v_CASP8 -v_ProCASP8 -> v_CASP8 -v_FASLG_FAS_FADD_FAF1_DAXX_CFLAR -> v_CASP8 -v_TNFSF10_TNFRSF10A_FADD -> v_CASP8 -v_TP53nucleus -> v_FASLGgene -v_IRF1_IRF1nucleus -> v_FASLGgene -v_Mitochondrial_Activator -> v_BAX -v_Fragmented_DNAnucleus -> v_DNA_Fragmentation -v_IDFFA -> v_DNA_Fragmentation -v_CD40LG -> v_CD40_CD40LG -v_CD40 -> v_CD40_CD40LG -v_External_Activator -> v_IRF4 -v_External_Activator -> v_MYD88 -v_FADD -> v_FASLG_FAS_FADD_FAF1_DAXX -v_FASLG_FAS -> v_FASLG_FAS_FADD_FAF1_DAXX -v_FAF1 -> v_FASLG_FAS_FADD_FAF1_DAXX -v_DAXX -> v_FASLG_FAS_FADD_FAF1_DAXX -v_IRF5_IRF5nucleus -> v_SP100gene -v_dsRNA -> v_TLR3_TLR3 -v_TLR3 -> v_TLR3_TLR3 -v_RELB -> v_NFKB2p100_RELBcytoplasm -v_CHUK_CHUK -> v_NFKB2p100_RELBcytoplasm -v_NFKB2p100 -> v_NFKB2p100_RELBcytoplasm -v_IRF1_IRF1nucleus -> v_PSMB9gene -v_IRF2 -> v_PSMB9gene -v_External_Activator -> v_PARP -v_External_Activator -> v_ProCASP10 -v_FADD -> v_TNFSF10_TNFSF10B_FADD -v_TNFSF10_TNFSF10B -> v_TNFSF10_TNFSF10B_FADD -v_TLR9_MYD88_TRAF6_IRF5 -> v_IRF5 -v_Virus -> v_IRF5 -v_TLR7_MYD88_TRAF6_IRF5 -> v_IRF5 -v_TNFRSF10A -> v_TNFSF10_TNFRSF10A -v_TNFSF10 -> v_TNFSF10_TNFRSF10A -v_IRF1_IRF1nucleus -> v_IRF2gene -v_IKBKG_CHUK_IKBKB -> v_NFKB2p100_NFKB2p100cytoplasm -v_NFKB2p100 -> v_NFKB2p100_NFKB2p100cytoplasm -v_External_Activator -> v_BCL3 -v_TP53nucleus -> v_IFR5gene -v_STAT1_STAT1_IRF9nucleus -> v_IFR5gene -v_External_Activator -> v_EP300 -v_TRAF6 -> v_ILIB_IL1R1_MYD88_IRAK1_IRAK4_TRAF6 -v_ILIB_IL1R1_MYD88_IRAK1_IRAK4 -> v_ILIB_IL1R1_MYD88_IRAK1_IRAK4_TRAF6 -v_LPS -> v_TLR4 -v_IRF2 -|? v_HLA_Bgene -v_IRF8 -> v_HLA_Bgene -v_EIF2AK2gene -> v_EIF2S1 -v_EIF2AK2cytoplasm -> v_EIF2AK2_PRKRA -v_PRKRA -> v_EIF2AK2_PRKRA -v_NFKB2p100_NFKB2p100cytoplasm -> v_NFKB2p100_NFKB2p100_Ub -v_Ub -> v_NFKB2p100_NFKB2p100_Ub -v_FADD -> v_TNF_TNFRSF1A_FADD_TRADD -v_TRADD -> v_TNF_TNFRSF1A_FADD_TRADD -v_TNF_TNFRSF1A -> v_TNF_TNFRSF1A_FADD_TRADD -v_External_Activator -> v_MAP3K7 -v_External_Activator -> v_IRF9 -v_CASP4 -> v_CASP1 -v_ProCASP1 -> v_CASP1 -v_BAK1 -> v_DIABLOcytoplasm -v_tBID -> v_DIABLOcytoplasm -v_DIABLOmitochondria -> v_DIABLOcytoplasm -v_BAX -> v_DIABLOcytoplasm -v_Mitochondrial_Activator -> v_PDCD8mitochondria -v_STAT1_STAT1nucleus_p2 -> v_IRF1gene -v_External_Activator -> v_IRAK1 -v_STAT1_STAT1cytoplasm -> v_STAT1_STAT1nucleus_p1 -v_External_Activator -> v_NFKB2p100 -v_IRF5_IRF5cytoplasm -> v_IRF5_IRF5nucleus -v_IFNA -> v_IFNA_IFNA -v_Bacteria -> v_Triacyl_Lipopeptides -v_ENDOGnucleus -> v_Fragmented_DNAnucleus -v_PDCD8nucleus -> v_Fragmented_DNAnucleus -v_DNA -> v_Fragmented_DNAnucleus -v_TLR3_TICAM1_TICAM2_RIPK1_RIPK3 -> v_IKBKG_CHUK_IKBKB -v_CHUK -> v_IKBKG_CHUK_IKBKB -v_IKBKB -> v_IKBKG_CHUK_IKBKB -v_MAP3K7IP1_MAP3K7IP2_MAP3K7 -> v_IKBKG_CHUK_IKBKB -v_IKBKG -> v_IKBKG_CHUK_IKBKB -v_IRF1_IRF1_Activator -> v_PKA -v_Mitochondrial_Activator -> v_DIABLOmitochondria -v_External_Activator -> v_IKBKE -v_Mitochondrial_Activator -> v_BCL2L1mitochondria -v_TLR7 -> v_TLR7_TLR7 -v_ssRNA -> v_TLR7_TLR7 -v_TNFSF13B_TNFRSF17 -> v_TNFSF13B_TNFRSF17_TRAF5 -v_TRAF5 -> v_TNFSF13B_TNFRSF17_TRAF5 -v_CASP8 -> v_CASP3cytoplasm -v_ProCASP3 -> v_CASP3cytoplasm -v_CASP4 -> v_CASP3cytoplasm -v_BIRC2 -|? v_CASP3cytoplasm -v_BIRC4cytoplasm -|? v_CASP3cytoplasm -v_Apoptosome -> v_CASP3cytoplasm -v_Mitochondrial_Activator -> v_HTRA2mitochondria -v_External_Activator -> v_SOCS1 -v_TLR9 -> v_TLR9_TLR9 -v_Viral_Bacterial_CpG -> v_TLR9_TLR9 -v_External_Activator -> v_TRAF2cytoplasm -v_TLR7_TLR7 -> v_TLR7_MYD88_IRAK1_IRAK4 -v_IRAK1 -> v_TLR7_MYD88_IRAK1_IRAK4 -v_MYD88 -> v_TLR7_MYD88_IRAK1_IRAK4 -v_IRAK4 -> v_TLR7_MYD88_IRAK1_IRAK4 -v_RELAp65_NFBK1p50nucleus -> v_SOD2gene -v_RELAp65_NFBK1p50nucleus -> v_BIRC3gene -v_External_Activator -> v_PTPN2 -v_TRAF6 -> v_TLR5_MYD88_IRAK1_IRAK4_TRAF6 -v_TLR5_MYD88_IRAK1_IRAK4 -> v_TLR5_MYD88_IRAK1_IRAK4_TRAF6 -v_RELA_NFKB1_Activator -> v_NFKBIA -v_NFKBIA_RELAp65_NFKB1p50 -> v_NFKBIA -v_Ub -|? v_NFKBIA -v_External_Activator -> v_IFNGR2 -v_External_Activator -> v_TLR3 -v_IRF3_IRF7cytoplasm -> v_IRF3_IRF7nucleus -v_IRF3 -> v_IRF3_IRF3cytoplasm -v_Bacteria -> v_Diacyl_Lipopeptides -v_tBID -> v_HTRA2cytoplasm -v_HTRA2mitochondria -> v_HTRA2cytoplasm -v_CASP3cytoplasm -> v_CASP3gene -v_IRF5_IRF5nucleus -> v_CASP3gene -v_External_Activator -> v_RPS6KA5 -v_CASP3cytoplasm -> v_CASP3nucleus -v_IRF3_IRF5cytoplasm -> v_IRF3_IRF5nucleus -v_TNF_TNFRSF1A_FADD_TRADD_TRAF2_RIPK1 -> v_TNF_IKBKG_Complex -v_CHUK -> v_TNF_IKBKG_Complex -v_IKBKB -> v_TNF_IKBKG_Complex -v_IKBKG -> v_TNF_IKBKG_Complex -v_Mitochondrial_Activator -> v_ENDOGmitochondria -v_Proteasome -> v_NFKB2p52_RELBcytoplasm -v_NFKB2p100_RELB_Ub -> v_NFKB2p52_RELBcytoplasm -v_TNF_TNFRSF1B_TRAF2 -> v_MAP3K14 -v_TNFSF13B_TNFRSF17_TRAF5 -> v_MAP3K14 -v_CD40_CD40LG_TRAF3 -> v_MAP3K14 -v_CYCScytoplasm -> v_APAF1_CYCS -v_APAF1 -> v_APAF1_CYCS -v_TLR3_TLR3 -> v_TLR3_TICAM1_TICAM2 -v_TICAM2 -> v_TLR3_TICAM1_TICAM2 -v_TICAM1 -> v_TLR3_TICAM1_TICAM2 -v_TP53nucleus -> v_TNFRSF10Bgene -v_IRF5_IRF5nucleus -> v_IFIT1gene -v_IFNB1 -> v_IFNB1_IFNB1 -v_RELAp65_NFBK1p50nucleus -> v_TRAF1gene -v_IRF1_IRF1_Activator -> v_PKC -v_External_Activator -> v_IFNAR1 -v_External_Activator -> v_TRAF6 -v_RELAp65_NFBK1p50nucleus -> v_IER3gene -v_External_Activator -> v_MAP3K7IP1 -v_External_Activator -> v_FADD -v_IRF1_IRF1nucleus -> v_IRF8 -v_IRF2 -> v_IRF8 -v_External_Activator -> v_BAG4 -v_TNF_BAG4_TNFRSF1A -> v_BAG4 -v_TLR3_TICAM1_TICAM2_TBK1 -> v_IRF3 -v_IKBKE_TBK1 -> v_IRF3 -v_External_Activator -> v_DNA -v_IRF1_IRF1nucleus -> v_CCL5gene -$v_APAF1: v_External_Activator -$v_APAF1_CYCS: v_APAF1 & v_CYCScytoplasm -$v_APAF1gene: v_TP53nucleus -$v_ATF2: v_External_Activator -$v_Apoptosis: v_DNA_Fragmentation | v_Inactive_DNA_Repair | v_Cell_Shrinkage | v_Inactivation_of_Protein_Synthesis -$v_Apoptosome: v_APAF1_CYCS & v_CASP9 -$v_BAD: v_Mitochondrial_Activator -$v_BAG4: v_External_Activator | v_TNF_BAG4_TNFRSF1A -$v_BAG4_TNFRSF1A: v_BAG4 & v_TNFRSF1A -$v_BAK1: v_Mitochondrial_Activator -$v_BAK1gene: v_IRF5_IRF5nucleus -$v_BAX: v_Mitochondrial_Activator -$v_BBC3gene: v_TP53nucleus -$v_BCL2: v_Mitochondrial_Activator -$v_BCL2A1: v_RELAp65_NFBK1p50nucleus -$v_BCL2A1gene: v_RELAp65_NFBK1p50nucleus -$v_BCL2L1_BAD: v_BCL2L1mitochondria & v_BAD -$v_BCL2L1gene: v_RELAp65_NFBK1p50nucleus -$v_BCL2L1mitochondria: v_Mitochondrial_Activator -$v_BCL2_BAX: v_BCL2 & v_BAX -$v_BCL3: v_External_Activator -$v_BCL3_NFKB2p52_NFKB2p52: v_BCL3 & v_NFKB2p52_NFKB2p52nucleus -$v_BID: v_External_Activator -$v_BIRC2: v_External_Activator -$v_BIRC3gene: v_RELAp65_NFBK1p50nucleus -$v_BIRC4cytoplasm: !(v_DIABLOcytoplasm | v_HTRA2cytoplasm) -$v_BIRC4gene: v_RELAp65_NFBK1p50nucleus -$v_CASP1: v_ProCASP1 & v_CASP4 -$v_CASP10: v_ProCASP10 & v_TNF_TNFRSF1A_FADD_TRADD -$v_CASP2: v_ProCASP2 & v_Fragmented_DNAcytoplasm -$v_CASP3cytoplasm: v_ProCASP3 & (v_CASP8 | v_CASP4 | v_Apoptosome) -$v_CASP3gene: v_IRF5_IRF5nucleus | v_CASP3cytoplasm -$v_CASP3nucleus: v_CASP3cytoplasm -$v_CASP4: v_ProCASP4 -$v_CASP6cytoplasm: v_ProCASP6 & v_CASP9 -$v_CASP6nucleus: v_CASP6cytoplasm -$v_CASP7cytoplasm: v_ProCASP7 & (v_CASP8 | v_Apoptosome | v_CASP10) -$v_CASP7nucleus: v_CASP7cytoplasm -$v_CASP8: v_ProCASP8 & (v_TNFSF10_TNFSF10B_FADD | v_FASLG_FAS_FADD_FAF1_DAXX | v_FASLG_FAS_FADD_FAF1_DAXX_CFLAR | v_TNFSF10_TNFRSF10A_FADD) -$v_CASP9: v_CASP6_Activator -$v_CBP: v_EP300 & v_CREBBP -$v_CCL5gene: v_IRF1_IRF1nucleus -$v_CD40: v_External_Activator -$v_CD40_CD40LG: v_CD40 & v_CD40LG -$v_CD40_CD40LG_TRAF3: v_CD40_CD40LG & v_TRAF3 -$v_CFLAR: v_External_Activator -$v_CFLARgene: v_RELAp65_NFBK1p50nucleus -$v_CHUK: v_External_Activator -$v_CHUK_CHUK: v_CHUK & v_MAP3K14 -$v_CIITAgene: v_IRF1_IRF1nucleus | v_IRF2 -$v_CKII: v_IRF1_IRF1_Activator -$v_CREBBP: v_External_Activator -$v_CXCL10gene: v_ISGF3nucleus | (v_CBP & (v_IRF3_IRF7nucleus | v_IRF3_IRF3nucleus)) -$v_CXCL11gene: v_IRF5_IRF5nucleus -$v_CXCL9gene: v_STAT1_STAT1nucleus_p2 -$v_CYBBgene: v_IRF8 & v_SPI1 -$v_CYCScytoplasm: v_CYCSmytochondria & (v_BAK1 | v_tBID | v_BAX) -$v_CYCSmytochondria: v_Mitochondrial_Activator -$v_Cell_Shrinkage: v_cGAS2 | v_cLMNA -$v_DAXX: v_External_Activator -$v_DFFA: v_External_Activator -$v_DIABLOcytoplasm: v_DIABLOmitochondria & (v_BAK1 | v_tBID | v_BAX) -$v_DIABLOmitochondria: v_Mitochondrial_Activator -$v_DNA: v_External_Activator -$v_DNA_Fragmentation: v_Fragmented_DNAnucleus | v_IDFFA -$v_Diacyl_Lipopeptides: v_Bacteria -$v_EIF2AK2_PRKRA: v_EIF2AK2cytoplasm & v_PRKRA -$v_EIF2AK2cytoplasm: v_dsRNA -$v_EIF2AK2gene: v_ISGF3nucleus | v_IRF1_IRF1nucleus -$v_EIF2S1: v_EIF2AK2gene -$v_ENDOGcytoplasm: v_tBID & v_ENDOGmitochondria -$v_ENDOGmitochondria: v_Mitochondrial_Activator -$v_ENDOGnucleus: v_ENDOGcytoplasm -$v_EP300: v_External_Activator -$v_FADD: v_External_Activator -$v_FAF1: v_External_Activator -$v_FAS: v_External_Activator -$v_FASLG_FAS: v_FASLG & v_FAS -$v_FASLG_FAS_FADD_FAF1_DAXX: v_FASLG_FAS & v_FADD & v_FAF1 & v_DAXX -$v_FASLG_FAS_FADD_FAF1_DAXX_CFLAR: v_FASLG_FAS_FADD_FAF1_DAXX & v_CFLAR -$v_FASLGgene: v_IRF1_IRF1nucleus | v_TP53nucleus -$v_FASgene: v_RELAp65_NFBK1p50nucleus -$v_Flagellin: v_Bacteria -$v_Fragmented_DNAcytoplasm: v_DNA_Fragmentation -$v_Fragmented_DNAnucleus: v_DNA & (v_ENDOGnucleus | v_PDCD8nucleus) -$v_G1P2gene: v_ISGF3nucleus | (v_IRF2 & v_SPI1 & v_IRF4 & v_IRF8) | (v_CBP & (v_IRF3_IRF7nucleus | v_IRF3_IRF3nucleus)) | (v_IRF1_IRF1nucleus & v_SPI1 & v_IRF4 & v_IRF8) -$v_G1P3gene: v_IRF8 -$v_GAS2: v_External_Activator -$v_GBP1gene: v_IRF2 | v_IRF1_IRF1nucleus -$v_HIST2H4gene: v_IRF1_IRF1nucleus | v_IRF2 -$v_HLA_Bgene: v_IRF8 -$v_HSPA1A: v_External_Activator -$v_HTRA2cytoplasm: v_tBID & v_HTRA2mitochondria -$v_HTRA2mitochondria: v_Mitochondrial_Activator -$v_ICAM1gene: v_STAT1_STAT1nucleus_p2 -$v_IDFFA: v_DFFA & v_CASP3nucleus -$v_IER3gene: v_RELAp65_NFBK1p50nucleus -$v_IFIT1gene: v_IRF5_IRF5nucleus -$v_IFIT2gene: (v_CBP & (v_IRF3_IRF7nucleus | v_IRF3_IRF3nucleus)) | v_ISGF3nucleus -$v_IFNAR: (v_IFNA & v_TYK2 & v_IFNAR1 & v_IFNAR2 & v_JAK1) | (v_IFNB1 & v_TYK2 & v_IFNAR1 & v_IFNAR2 & v_JAK1) -$v_IFNAR1: v_External_Activator -$v_IFNAR2: v_External_Activator -$v_IFNA_IFNA: v_IFNA -$v_IFNAgene: v_IRF5_IRF5nucleus | v_IRF7_IRF7nucleus | v_IRF3_IRF5nucleus | v_STAT1_STAT1_IRF9nucleus | v_IRF1_IRF1nucleus | v_IRF3_IRF3nucleus | v_IRF3_IRF7nucleus | v_ISGF3nucleus -$v_IFNB1_IFNB1: v_IFNB1 -$v_IFNB1gene: (v_RELAp65_NFBK1p50nucleus & v_IRF3_IRF7nucleus & v_IRF3_IRF3nucleus & v_ATF2 & v_CBP) | v_IRF5_IRF5nucleus | v_IRF1_IRF1nucleus | v_IRF3_IRF3nucleus | v_ISGF3nucleus | v_STAT1_STAT1nucleus_p2 -$v_IFNGR: v_IFNG & v_IFNGR2 & v_JAK2 & v_IFNGR1 -$v_IFNGR1: v_External_Activator -$v_IFNGR2: v_External_Activator -$v_IFR5gene: v_STAT1_STAT1_IRF9nucleus | v_TP53nucleus -$v_IKBKB: v_External_Activator -$v_IKBKE: v_External_Activator -$v_IKBKE_TBK1: v_TLR4_TICAM1_TICAM2 & v_IKBKE & v_TBK1 -$v_IKBKG: v_External_Activator -$v_IKBKG_CHUK_IKBKB: (v_TLR3_TICAM1_TICAM2_RIPK1_RIPK3 & v_CHUK & v_IKBKB & v_IKBKG) | (v_IKBKG & v_CHUK & v_IKBKB & v_MAP3K7IP1_MAP3K7IP2_MAP3K7) -$v_IL12Bgene: v_IRF1_IRF1nucleus -$v_IL15gene: v_IRF1_IRF1nucleus -$v_IL1Bgene: v_IRF8 & v_SPI1 -$v_IL1R1: v_External_Activator -$v_ILIB_IL1R1: v_ILIB & v_IL1R1 -$v_ILIB_IL1R1_MYD88_IRAK1_IRAK4: v_ILIB_IL1R1 & v_IRAK1 & v_MYD88 & v_IRAK4 -$v_ILIB_IL1R1_MYD88_IRAK1_IRAK4_TRAF6: v_ILIB_IL1R1_MYD88_IRAK1_IRAK4 & v_TRAF6 -$v_IPARP: v_PARP & (v_CASP7nucleus | v_CASP3nucleus) -$v_IRAK1: v_External_Activator -$v_IRAK2: v_External_Activator -$v_IRAK4: v_External_Activator -$v_IRF1_IRF1cytoplasm: v_IRF1cytoplasm -$v_IRF1_IRF1nucleus: v_IRF1_IRF1cytoplasm -$v_IRF1cytoplasm: v_PKA | v_CKII | v_PKC -$v_IRF1gene: v_STAT1_STAT1nucleus_p2 -$v_IRF2: v_IRF2_Activator -$v_IRF2gene: v_IRF1_IRF1nucleus -$v_IRF3: v_TLR3_TICAM1_TICAM2_TBK1 | v_IKBKE_TBK1 -$v_IRF3_IRF3cytoplasm: v_IRF3 -$v_IRF3_IRF3nucleus: v_IRF3_IRF3cytoplasm -$v_IRF3_IRF5cytoplasm: v_IRF5 -$v_IRF3_IRF5nucleus: v_IRF3_IRF5cytoplasm -$v_IRF3_IRF7cytoplasm: v_IRF3 & v_IRF7 -$v_IRF3_IRF7nucleus: v_IRF3_IRF7cytoplasm -$v_IRF4: v_External_Activator -$v_IRF5: v_TLR7_MYD88_TRAF6_IRF5 | v_Virus | v_TLR9_MYD88_TRAF6_IRF5 -$v_IRF5_IRF5cytoplasm: v_IRF5 -$v_IRF5_IRF5nucleus: v_IRF5_IRF5cytoplasm -$v_IRF5_IRF7cytoplasm: v_IRF5 -$v_IRF5_IRF7nucleus: v_IRF5_IRF7cytoplasm -$v_IRF7: v_IKBKE_TBK1 -$v_IRF7_IRF7cytoplasm: v_IRF7 -$v_IRF7_IRF7nucleus: v_IRF7_IRF7cytoplasm -$v_IRF8: v_IRF1_IRF1nucleus | v_IRF2 -$v_IRF9: v_External_Activator -$v_ISGF3cytoplasm: v_STAT1_STAT2 & v_IRF9 -$v_ISGF3nucleus: v_ISGF3cytoplasm -$v_Inactivation_of_Protein_Synthesis: v_EIF2S1 -$v_Inactive_DNA_Repair: v_IPARP -$v_JAK1: v_External_Activator -$v_JAK2: v_External_Activator -$v_LMNA: v_External_Activator -$v_LPS: v_Bacteria -$v_MAP3K14: v_CD40_CD40LG_TRAF3 | v_TNF_TNFRSF1B_TRAF2 | v_TNFSF13B_TNFRSF17_TRAF5 -$v_MAP3K7: v_External_Activator -$v_MAP3K7IP1: v_External_Activator -$v_MAP3K7IP1_MAP3K7IP2_MAP3K7: (v_ILIB_IL1R1_MYD88_IRAK1_IRAK4_TRAF6 & v_MAP3K7IP1 & v_MAP3K7IP2 & v_MAP3K7) | (v_TLR7_MYD88_IRAK1_IRAK4_TRAF6 & v_MAP3K7IP1 & v_MAP3K7IP2 & v_MAP3K7) | (v_TLR3_TICAM1_TICAM2_TRAF6 & v_MAP3K7IP1 & v_MAP3K7IP2 & v_MAP3K7) | (v_TLR9_MYD88_IRAK1_IRAK4_TRAF6 & v_MAP3K7IP1 & v_MAP3K7IP2 & v_MAP3K7) | (v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4_TRAF6 & v_MAP3K7IP1 & v_MAP3K7IP2 & v_MAP3K7) | (v_TLR5_MYD88_IRAK1_IRAK4_TRAF6 & v_MAP3K7IP1 & v_MAP3K7IP2 & v_MAP3K7) | (v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4_TRAF6 & v_MAP3K7IP1 & v_MAP3K7IP2 & v_MAP3K7) -$v_MAP3K7IP2: v_External_Activator -$v_MDM2gene: v_TP53nucleus -$v_MYD88: v_External_Activator -$v_NFKB1_p50: v_RELA_NFKB1_Activator -$v_NFKB2p100: v_External_Activator -$v_NFKB2p100_NFKB2p100_Ub: v_NFKB2p100_NFKB2p100cytoplasm & v_Ub -$v_NFKB2p100_NFKB2p100cytoplasm: v_NFKB2p100 & v_IKBKG_CHUK_IKBKB -$v_NFKB2p100_RELB_Ub: v_NFKB2p100_RELBcytoplasm & v_Ub -$v_NFKB2p100_RELBcytoplasm: v_NFKB2p100 & v_RELB & v_CHUK_CHUK -$v_NFKB2p52_NFKB2p52cytoplasm: v_NFKB2p100_NFKB2p100_Ub & v_Proteasome -$v_NFKB2p52_NFKB2p52nucleus: v_NFKB2p52_NFKB2p52cytoplasm -$v_NFKB2p52_RELBcytoplasm: v_NFKB2p100_RELB_Ub & v_Proteasome -$v_NFKB2p52_RELBnucleus: v_NFKB2p52_RELBcytoplasm -$v_NFKBIA: v_RELA_NFKB1_Activator | v_NFKBIA_RELAp65_NFKB1p50 -$v_NFKBIA_RELAp65_NFKB1p50: (v_TNF_IKBKG_Complex & v_NFKBIA & v_RELA_p65 & v_NFKB1_p50) | (v_IKBKG_CHUK_IKBKB & v_NFKBIA & v_RELA_p65 & v_NFKB1_p50) -$v_NOS2Agene: v_External_Activator -$v_OAS1gene: v_ISGF3nucleus | v_IRF5_IRF5nucleus | v_IRF8 | v_IRF1_IRF1nucleus -$v_PARP: v_External_Activator -$v_PDCD8_HSPA1A: v_PDCD8cytoplasm & v_HSPA1A -$v_PDCD8cytoplasm: v_tBID & v_PDCD8mitochondria -$v_PDCD8mitochondria: v_Mitochondrial_Activator -$v_PDCD8nucleus: v_PDCD8cytoplasm -$v_PKA: v_IRF1_IRF1_Activator -$v_PKC: v_IRF1_IRF1_Activator -$v_PLSCR1gene: v_IRF5_IRF5nucleus -$v_PMAIPgene: v_IRF5_IRF5nucleus -$v_PRKCD: v_External_Activator -$v_PRKCZ: v_External_Activator -$v_PRKRA: v_External_Activator -$v_PRKRAgene: v_IRF5_IRF5nucleus -$v_PSMB9gene: v_IRF1_IRF1nucleus | v_IRF2 -$v_PTP: v_External_Activator -$v_PTPN2: v_External_Activator -$v_ProCASP1: v_External_Activator -$v_ProCASP10: v_External_Activator -$v_ProCASP2: v_External_Activator -$v_ProCASP3: v_CASP3_Activator -$v_ProCASP4: v_External_Activator -$v_ProCASP6: v_CASP6_Activator -$v_ProCASP7: v_CASP7_Activator -$v_ProCASP8: v_External_Activator -$v_Proteasome: v_External_Activator -$v_RELA_p65: v_RELA_NFKB1_Activator -$v_RELAp65_NFBK1p50nucleus: v_RELAp65_NFKB1p50cytoplasm -$v_RELAp65_NFKB1p50cytoplasm: v_NFKBIA_RELAp65_NFKB1p50 & (v_TBK1 | v_CHUK | v_IKBKB | v_MAP3K7 | v_RPS6KA5 | v_PRKCZ) -$v_RELB: v_External_Activator -$v_RIPK1: v_External_Activator -$v_RIPK1gene: v_IRF5_IRF5nucleus -$v_RIPK3: v_External_Activator -$v_RPS6KA5: v_External_Activator -$v_SOCS1: v_External_Activator -$v_SOCS3: v_External_Activator -$v_SOD2gene: v_RELAp65_NFBK1p50nucleus -$v_SP100gene: v_IRF5_IRF5nucleus -$v_SPI1: v_External_Activator -$v_STAT1: v_IFNGR -$v_STAT1_STAT1_IRF9cytoplasm: v_STAT1 & v_IRF9 -$v_STAT1_STAT1_IRF9nucleus: v_STAT1_STAT1_IRF9cytoplasm -$v_STAT1_STAT1cytoplasm: v_STAT1 & v_PRKCD -$v_STAT1_STAT1nucleus_p1: v_STAT1_STAT1cytoplasm -$v_STAT1_STAT1nucleus_p2: v_STAT1_STAT1nucleus_p1 -$v_STAT1_STAT2: v_STAT1 & v_STAT2 -$v_STAT1gene: v_IRF1_IRF1nucleus -$v_STAT2: v_IFNAR -$v_TAP1gene: v_IRF2 | v_IRF1_IRF1nucleus -$v_TBK1: v_External_Activator -$v_TICAM1: v_External_Activator -$v_TICAM2: v_External_Activator -$v_TIRAP: v_External_Activator -$v_TLR1_TLR2: v_Triacyl_Lipopeptides -$v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4: v_TLR1_TLR2 & v_IRAK2 & v_TIRAP & v_IRAK1 & v_MYD88 & v_IRAK4 -$v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4_TRAF6: v_TLR1_TLR2_TIRAP_MYD88_IRAK2_IRAK1_IRAK4 & v_TRAF6 -$v_TLR2: v_External_Activator -$v_TLR2_TLR6: v_TLR2 & v_TLR6 & v_Diacyl_Lipopeptides -$v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4: v_TLR2_TLR6 & v_TOLLIP & v_IRAK1 & v_MYD88 & v_IRAK4 -$v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4_TRAF6: v_TLR2_TLR6_TOLLIP_MYD88_IRAK1_IRAK4 & v_TRAF6 -$v_TLR3: v_External_Activator -$v_TLR3_TICAM1_TICAM2: v_TLR3_TLR3 & v_TICAM2 & v_TICAM1 -$v_TLR3_TICAM1_TICAM2_RIPK1: v_TLR3_TICAM1_TICAM2 & v_RIPK1 -$v_TLR3_TICAM1_TICAM2_RIPK1_RIPK3: v_TLR3_TICAM1_TICAM2_RIPK1 & v_RIPK3 -$v_TLR3_TICAM1_TICAM2_TBK1: v_TLR3_TICAM1_TICAM2 & v_TBK1 -$v_TLR3_TICAM1_TICAM2_TRAF6: v_TLR3_TICAM1_TICAM2 & v_TRAF6 -$v_TLR3_TLR3: v_dsRNA & v_TLR3 -$v_TLR4: v_LPS -$v_TLR4_TICAM1_TICAM2: v_TLR4 & v_TICAM2 & v_TICAM1 -$v_TLR5: v_External_Activator -$v_TLR5_MYD88_IRAK1_IRAK4: v_TLR5_TLR5 & v_IRAK1 & v_MYD88 & v_IRAK4 -$v_TLR5_MYD88_IRAK1_IRAK4_TRAF6: v_TLR5_MYD88_IRAK1_IRAK4 & v_TRAF6 -$v_TLR5_TLR5: v_TLR5 & v_Flagellin -$v_TLR6: v_External_Activator -$v_TLR7: v_External_Activator -$v_TLR7_MYD88_IRAK1_IRAK4: v_TLR7_TLR7 & v_IRAK1 & v_MYD88 & v_IRAK4 -$v_TLR7_MYD88_IRAK1_IRAK4_TRAF6: v_TLR7_MYD88_IRAK1_IRAK4 & v_TRAF6 -$v_TLR7_MYD88_TRAF6_IRF5: v_TLR7_TLR7 & v_TRAF6 & v_MYD88 & !v_IRF5 -$v_TLR7_TLR7: v_TLR7 & v_ssRNA -$v_TLR9: v_External_Activator -$v_TLR9_MYD88_IRAK1_IRAK4: v_TLR9_TLR9 & v_IRAK1 & v_MYD88 & v_IRAK4 -$v_TLR9_MYD88_IRAK1_IRAK4_TRAF6: v_TLR9_MYD88_IRAK1_IRAK4 & v_TRAF6 -$v_TLR9_MYD88_TRAF6: v_TLR9_MYD88_IRAK1_IRAK4_TRAF6 -$v_TLR9_MYD88_TRAF6_IRF5: v_TLR9_TLR9 & v_TRAF6 & v_MYD88 & !v_IRF5 -$v_TLR9_TLR9: v_TLR9 & v_Viral_Bacterial_CpG -$v_TNFRSF10A: v_External_Activator -$v_TNFRSF10B: v_External_Activator -$v_TNFRSF10Bgene: v_TP53nucleus -$v_TNFRSF17: v_External_Activator -$v_TNFRSF1A: v_External_Activator -$v_TNFRSF1B: v_External_Activator -$v_TNFSF10_TNFRSF10A: v_TNFSF10 & v_TNFRSF10A -$v_TNFSF10_TNFRSF10A_FADD: v_TNFSF10_TNFRSF10A & v_FADD -$v_TNFSF10_TNFSF10B: v_TNFSF10 & v_TNFRSF10B -$v_TNFSF10_TNFSF10B_FADD: v_TNFSF10_TNFSF10B & v_FADD -$v_TNFSF10gene: v_IRF5_IRF5nucleus | v_IRF1_IRF1nucleus -$v_TNFSF13B_TNFRSF17: v_TNFSF13B & v_TNFRSF17 -$v_TNFSF13B_TNFRSF17_TRAF5: v_TNFSF13B_TNFRSF17 & v_TRAF5 -$v_TNF_BAG4_TNFRSF1A: v_TNF & v_BAG4_TNFRSF1A -$v_TNF_IKBKG_Complex: v_TNF_TNFRSF1A_FADD_TRADD_TRAF2_RIPK1 & v_CHUK & v_IKBKB & v_IKBKG -$v_TNF_TNFRSF1A: v_TNF_BAG4_TNFRSF1A -$v_TNF_TNFRSF1A_FADD_TRADD: v_TNF_TNFRSF1A & v_FADD & v_TRADD -$v_TNF_TNFRSF1A_FADD_TRADD_TRAF2_RIPK1: v_TNF_TNFRSF1A_FADD_TRADD & v_RIPK1 & v_TRAF2cytoplasm -$v_TNF_TNFRSF1B: v_TNF & v_TNFRSF1B -$v_TNF_TNFRSF1B_TRAF2: v_TNF_TNFRSF1B & v_TRAF2cytoplasm -$v_TOLLIP: v_External_Activator -$v_TP53cytoplasm: v_EIF2AK2_PRKRA -$v_TP53nucleus: v_TP53cytoplasm -$v_TRADD: v_External_Activator -$v_TRAF1gene: v_RELAp65_NFBK1p50nucleus -$v_TRAF2cytoplasm: v_External_Activator -$v_TRAF2gene: v_RELAp65_NFBK1p50nucleus -$v_TRAF3: v_External_Activator -$v_TRAF5: v_External_Activator -$v_TRAF6: v_External_Activator -$v_TYK2: v_External_Activator -$v_Triacyl_Lipopeptides: v_Bacteria -$v_Ub: v_External_Activator -$v_Viral_Bacterial_CpG: v_Virus | v_Bacteria -$v_cGAS2: v_GAS2 & v_CASP3nucleus -$v_cLMNA: v_LMNA & v_CASP6nucleus -$v_dsRNA: v_Virus -$v_ssRNA: v_Virus -$v_tBID: v_BID & (v_CASP8 | v_CASP2) diff --git a/experiments/Synth/test/007.aeon b/experiments/Synth/test/007.aeon deleted file mode 100644 index 867c00e..0000000 --- a/experiments/Synth/test/007.aeon +++ /dev/null @@ -1,19 +0,0 @@ -v_Sp8 -| v_Emx2 -v_Coup_fti -> v_Emx2 -v_Pax6 -| v_Emx2 -v_Fgf8 -| v_Emx2 -v_Emx2 -| v_Sp8 -v_Fgf8 -> v_Sp8 -v_Sp8 -| v_Coup_fti -v_Fgf8 -| v_Coup_fti -v_Sp8 -> v_Pax6 -v_Coup_fti -| v_Pax6 -v_Emx2 -| v_Pax6 -v_Sp8 -> v_Fgf8 -v_Emx2 -| v_Fgf8 -v_Fgf8 -> v_Fgf8 -$v_Coup_fti: !(v_Fgf8 | v_Sp8) | !(v_Sp8 | v_Fgf8) -$v_Emx2: v_Coup_fti & !(v_Fgf8 | v_Sp8 | v_Pax6) -$v_Fgf8: v_Fgf8 & v_Sp8 & !v_Emx2 -$v_Pax6: v_Sp8 & !(v_Emx2 | v_Coup_fti) -$v_Sp8: v_Fgf8 & !v_Emx2 diff --git a/experiments/Synth/test/ex_model.jld2 b/experiments/Synth/test/ex_model.jld2 deleted file mode 100644 index 6b92fb7..0000000 Binary files a/experiments/Synth/test/ex_model.jld2 and /dev/null differ diff --git a/experiments/Synth/test/runtests.jl b/experiments/Synth/test/runtests.jl deleted file mode 100644 index 86fe3ea..0000000 --- a/experiments/Synth/test/runtests.jl +++ /dev/null @@ -1,28 +0,0 @@ -using DrWatson, Test -@quickactivate :Synth - -using HerbGrammar, HerbConstraints -using GraphDynamicalSystems: BooleanNetworks -using DynamicalSystems: StateSpaceSet -using JLD2 - -# Run test suite -println("Starting tests") -ti = time() - -testdir = (x...) -> projectdir("test", x...) - -@testset "Synth tests" begin - @testset "Sample Trajectories" begin - @unpack model = load(joinpath(@__DIR__, "ex_model.jld2")) - async_bn = BooleanNetworks.abn(model.graph) - @test !isnothing(gather_bn_data(async_bn, 1)) - end - - include(testdir("test_neighbor_counting.jl")) - include(testdir("test_biodivine_pipeline.jl")) -end - -ti = time() - ti -println("\nTest took total time of:") -println(round(ti / 60, digits = 3), " minutes") diff --git a/experiments/Synth/test/test_biodivine_pipeline.jl b/experiments/Synth/test/test_biodivine_pipeline.jl deleted file mode 100644 index c51a728..0000000 --- a/experiments/Synth/test/test_biodivine_pipeline.jl +++ /dev/null @@ -1,64 +0,0 @@ -using Graphs: nv -using DynamicalSystems: dimension - -@testset "Biodivine Pipeline" begin - @kwdef struct ModelExpectedVals - file::Any - variables::Any - regulations::Any - inputs::Any - end - - to_test = [ - ModelExpectedVals(file = "007.aeon", variables = 5, regulations = 14, inputs = 0), - ModelExpectedVals( - file = "001.aeon", - variables = 302, - regulations = 533, - inputs = 19, - ), - ] - - for exp_vals in to_test - @testset "$(exp_vals.file)" begin - filepath = testdir(exp_vals.file) - parsed_model = AEONParser.parse_aeon_file(filepath) - update_functions::Vector{AEONParser.UpdateFunction} = - filter(x -> x isa AEONParser.UpdateFunction, parsed_model) - regulations::Vector{AEONParser.Regulation} = - filter(x -> x isa AEONParser.Regulation, parsed_model) - - @testset "AEON Parsing" begin - @test length(update_functions) == exp_vals.variables - @test length(regulations) == exp_vals.regulations - end - - mg = update_functions_to_network(update_functions, regulations) - - @testset "AEON to MG" begin - @test nv(mg) == exp_vals.variables + exp_vals.inputs - end - - trajectories = [] - n_trajectories = 3 - trajectory_length = 25 - - for i = 1:n_trajectories # 3 trajectories - async_bn = BooleanNetworks.abn(mg, seed = i) - push!(trajectories, gather_bn_data(async_bn, trajectory_length)) - end - - @testset "Sample Trajectories" begin - @test length(trajectories) == n_trajectories - @test all(length.(trajectories) .== trajectory_length + 1) - @test dimension(trajectories[1]) == nv(mg) - end - - split = split_state_space.(trajectories) - - @testset "Split Trajectories" begin - @test all((!isempty).(split)) - end - end - end -end diff --git a/experiments/Synth/test/test_neighbor_counting.jl b/experiments/Synth/test/test_neighbor_counting.jl deleted file mode 100644 index a0ae425..0000000 --- a/experiments/Synth/test/test_neighbor_counting.jl +++ /dev/null @@ -1,17 +0,0 @@ -@testset "Neighbor counting from RuleNode" begin - dnf = build_dnf_grammar(3) # rules 8, 9, and 10 are terminal - - r₁ = RuleNode(1, [RuleNode(8), RuleNode(8)]) # just 1 neighbor, used twice - r₂ = RuleNode(1, [RuleNode(8), RuleNode(9)]) # 2 neighbors - r₃ = RuleNode(1) # no neighbors - - @test count_neighbors_in_expr(r₁, dnf) == 1 - @test count_neighbors_in_expr(r₂, dnf) == 2 - @test count_neighbors_in_expr(r₃, dnf) == 0 - - rₛ = StateHole( - HerbConstraints.StateSparseSet(HerbConstraints.StateManager(), 1), - [RuleNode(8), RuleNode(9)], - ) - @test count_neighbors_in_expr(rₛ, dnf) == 2 -end diff --git a/experiments/Synth/test/test_state_division.jl b/experiments/Synth/test/test_state_division.jl deleted file mode 100644 index e89306c..0000000 --- a/experiments/Synth/test/test_state_division.jl +++ /dev/null @@ -1,11 +0,0 @@ -@testset "State division" begin - network_size = 10 - max_equation_depth = 3 - seed = 42 - iterations = 1000 - - bn = BooleanNetworks.sample_boolean_network(network_size, max_equation_depth, seed) - async_bn = BooleanNetworks.abn(bn; seed = seed) - trajectory = gather_bn_data(async_bn, iterations) - divided_state_space = split_state_space(trajectory) -end