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devmotiondevmotion
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Run examples in separate processes (#107)
Co-authored-by: David Widmann <devmotion@users.noreply.github.com>
1 parent 7f8f1a9 commit be24711

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‎.github/workflows/CompatHelper.yml‎

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@@ -2,17 +2,32 @@ name: CompatHelper
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on:
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schedule:
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- cron: '00 00 * * *'
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- cron: 0 0 * * *
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workflow_dispatch:
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jobs:
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CompatHelper:
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runs-on: ubuntu-latest
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steps:
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- name: Pkg.add("CompatHelper")
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run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
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- name: CompatHelper.main()
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- name: "Install CompatHelper"
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run: |
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import Pkg
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name = "CompatHelper"
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uuid = "aa819f21-2bde-4658-8897-bab36330d9b7"
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version = "2"
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Pkg.add(; name, uuid, version)
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shell: julia --color=yes {0}
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- name: "Run CompatHelper"
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run: |
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import CompatHelper
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subdirs = ["", "test", "docs"]
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for f in readdir(joinpath(@__DIR__, "examples"); join=true)
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if isdir(f)
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push!(subdirs, joinpath("examples", basename(f)))
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end
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end
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CompatHelper.main(; subdirs=subdirs)
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shell: julia --color=yes {0}
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }}
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run: julia -e 'using CompatHelper; CompatHelper.main(; subdirs = ["", "docs", "test", "examples/classification", "examples/distribution"])'
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COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}

‎.github/workflows/Docs.yml‎

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@@ -36,5 +36,7 @@ jobs:
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doctest(CalibrationErrors)'
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- run: julia --project=docs docs/make.jl
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env:
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DATADEPS_ALWAYS_ACCEPT: true # Bypass the accept download prompt
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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JULIA_DEBUG: Documenter # Print `@debug` statements (https://github.com/JuliaDocs/Documenter.jl/issues/955)
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}

‎docs/Manifest.toml‎

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‎docs/Project.toml‎

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[deps]
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CalibrationErrors = "33913031-fe46-5864-950f-100836f47845"
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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[compat]
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CalibrationErrors = "0.5.13"
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Documenter = "0.27"
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Literate = "2.7"
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julia = "1.5"

‎docs/literate.jl‎

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# Retrieve filename of literate script
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if length(ARGS) != 2
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error("please specify the literate script and the output directory")
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end
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const SCRIPTJL = ARGS[1]
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const OUTDIR = ARGS[2]
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# Activate environment
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using Pkg: Pkg
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Pkg.activate(dirname(SCRIPTJL))
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Pkg.instantiate()
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using Literate: Literate
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# Add link to nbviewer below the first heading of level 1
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function preprocess(content)
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sub = SubstitutionString(
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"""
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#md # ```@meta
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#md # EditURL = "@__REPO_ROOT_URL__/examples/@__NAME__/script.jl"
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#md # ```
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#md #
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\\0
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#
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#md # [![](https://img.shields.io/badge/show-nbviewer-579ACA.svg)](@__NBVIEWER_ROOT_URL__/examples/@__NAME__.ipynb)
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#md #
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# *You are seeing the
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#md # HTML output generated by [Documenter.jl](https://github.com/JuliaDocs/Documenter.jl) and
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#nb # notebook output generated by
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# [Literate.jl](https://github.com/fredrikekre/Literate.jl) from the
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# [Julia source file](@__REPO_ROOT_URL__/examples/@__NAME__/script.jl).
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# The corresponding
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#md # notebook can be viewed in [nbviewer](@__NBVIEWER_ROOT_URL__/examples/@__NAME__.ipynb).*
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#nb # HTML output can be viewed [here](https://devmotion.github.io/CalibrationErrors.jl/dev/examples/@__NAME__/).*
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#
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""",
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)
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return replace(content, r"^# # [^\n]*"m => sub; count=1)
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end
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# Convert to markdown and notebook
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Literate.markdown(
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SCRIPTJL,
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OUTDIR;
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name=basename(dirname(SCRIPTJL)),
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documenter=false,
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execute=true,
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preprocess=preprocess,
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)
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Literate.notebook(
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SCRIPTJL,
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OUTDIR;
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name=basename(dirname(SCRIPTJL)),
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documenter=false,
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execute=true,
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preprocess=preprocess,
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)

‎docs/make.jl‎

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@@ -1,61 +1,29 @@
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using CalibrationErrors
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using Literate: Literate
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using Pkg: Pkg
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if haskey(ENV, "GITHUB_ACTIONS")
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# Print `@debug` statements (https://github.com/JuliaDocs/Documenter.jl/issues/955)
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ENV["JULIA_DEBUG"] = "Documenter"
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# Bypass the accept download prompt
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ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"
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end
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const EXAMPLES = ("classification", "distribution")
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const INPUT = joinpath(@__DIR__, "..", "examples")
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const OUTPUT = joinpath(@__DIR__, "src", "examples")
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ispath(OUTPUT) && rm(OUTPUT; recursive=true)
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mkpath(joinpath(OUTPUT, "figures"))
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# Add link to nbviewer below the first heading of level 1
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function preprocess(content)
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sub = SubstitutionString(
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"""
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\\0
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#
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#md # [![](https://img.shields.io/badge/show-nbviewer-579ACA.svg)](@__NBVIEWER_ROOT_URL__/examples/@__NAME__.ipynb)
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#md #
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# You are seeing the
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#md # HTML output generated by [Documenter.jl](https://github.com/JuliaDocs/Documenter.jl) and
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#nb # notebook output generated by
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# [Literate.jl](https://github.com/fredrikekre/Literate.jl) from the
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# [Julia source file](@__REPO_ROOT_URL__/examples/@__NAME__/script.jl).
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# The corresponding
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#md # notebook can be viewed in [nbviewer](@__NBVIEWER_ROOT_URL__/examples/@__NAME__.ipynb).
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#nb # HTML output can be viewed [here](https://devmotion.github.io/CalibrationErrors.jl/dev/examples/@__NAME__/).
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#md #
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#md # ```@setup @__NAME__
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#md # using Pkg: Pkg
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#md # Pkg.activate("$(INPUT)/@__NAME__")
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#md # Pkg.instantiate()
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#md # ```
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#
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""",
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)
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return replace(content, r"^# # [^\n]*"m => sub; count=1)
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# Always rerun examples
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const EXAMPLES_OUT = joinpath(@__DIR__, "src", "examples")
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ispath(EXAMPLES_OUT) && rm(EXAMPLES_OUT; recursive=true)
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mkpath(EXAMPLES_OUT)
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# Run examples asynchronously
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const EXAMPLES_SRC = joinpath(@__DIR__, "..", "examples")
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const LITERATEJL = joinpath(@__DIR__, "literate.jl")
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processes = map(filter!(isdir, readdir(EXAMPLES_SRC; join=true))) do example
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scriptjl = joinpath(example, "script.jl")
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return run(
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pipeline(
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`$(Base.julia_cmd()) $LITERATEJL $scriptjl $EXAMPLES_OUT`;
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stdin=devnull,
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stdout=devnull,
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stderr=stderr,
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);
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wait=false,
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)::Base.Process
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end
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for name in EXAMPLES
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file = joinpath(INPUT, name, "script.jl")
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# Activate project environment
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Pkg.activate(dirname(file)) do
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Pkg.instantiate()
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Literate.markdown(file, OUTPUT; name=name, documenter=true, preprocess=preprocess)
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Literate.notebook(file, OUTPUT; name=name, documenter=true, preprocess=preprocess)
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end
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end
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# Check that all examples were run successfully
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isempty(processes) || success(processes) || error("some examples were not run successfully")
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# Build documentation
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using CalibrationErrors
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using Documenter
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makedocs(;
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"ece.md",
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"kce.md",
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"others.md",
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"Examples" => [joinpath("examples", "$(name).md") for name in EXAMPLES],
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"Examples" =>
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map(filter!(filename -> endswith(filename, ".md"), readdir(EXAMPLES_OUT))) do x
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return joinpath("examples", x)
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end,
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],
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strict=true,
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checkdocs=:exports,

‎docs/src/introduction.md‎

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@@ -10,15 +10,12 @@ target with absolute certainty) or caused by insufficient knowledge of the
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underlying relation between feature and target (e.g., if only a small number
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of observations of features and corresponding targets are available).[^1]
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The following plot shows measurements of the bill length and the flipper length
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for three different penguin species in the
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[Palmer penguins dataset](https://github.com/allisonhorst/palmerpenguins).
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![](./examples/figures/penguins.svg)
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There exist many different probabilistic predictive models for predicting the
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probability of the penguin species (*target*) given the bill and flipper length
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(*feature*). We denote the feature by $X$ and the target by $Y$, and let
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In the [classification example](../examples/classification) we study the
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[Palmer penguins dataset](https://github.com/allisonhorst/palmerpenguins) with
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measurements of three different penguin species and consider the task of predictin
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the probability of a penguin species (*target*) given the bill and flipper length
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(*feature*). For this classification task there exist many different probabilistic
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predictive models. We denote the feature by $X$ and the target by $Y$, and let
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$P_X$ be the prediction of a specific model $P$ for a feature $X$. Ideally, we would
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like that
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```math

‎examples/classification/Manifest.toml‎

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‎examples/classification/Project.toml‎

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[deps]
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AlgebraOfGraphics = "cbdf2221-f076-402e-a563-3d30da359d67"
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CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
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CalibrationErrors = "33913031-fe46-5864-950f-100836f47845"
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DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
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Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
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PalmerPenguins = "8b842266-38fa-440a-9b57-31493939ab85"
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Query = "1a8c2f83-1ff3-5112-b086-8aa67b057ba1"
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[compat]
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AlgebraOfGraphics = "0.4"
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CairoMakie = "0.6"
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CalibrationErrors = "0.5.13"
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DataFrames = "0.22.5, 1.0"
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Distributions = "0.24.15, 0.25"
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Literate = "2.8"
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PalmerPenguins = "0.1.2"
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Query = "1.0"
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julia = "1.3"

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