diff --git a/docs/make.jl b/docs/make.jl index 0b096e9be..7621381fa 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -6,67 +6,26 @@ using Documenter using Pluto, PlutoStaticHTML using Documenter: MathJax3 -tutorial_menu = Array{Pair{String,String},1}() +pluto_src_folder = joinpath(@__DIR__, "src", "tutorials") -# -# Generate Pluto Tutorial HTMLs +""" + build() -# pluto_src_folder = joinpath(@__DIR__, "src", "tutorials") -# pluto_output_folder = joinpath(@__DIR__, "src", "tutorials") -# pluto_relative_path = "tutorials/" -# mkpath(pluto_output_folder) +Run all Pluto notebooks (".jl" files) in `NOTEBOOK_DIR`. +""" +function build() + println("Building notebooks") + hopts = HTMLOptions(; append_build_context=false) + output_format = documenter_output + bopts = BuildOptions(pluto_src_folder; output_format) + build_notebooks(bopts, hopts) + return nothing +end -# """ -# build() -# Run all Pluto notebooks (".jl" files) in `NOTEBOOK_DIR`. -# """ -# function build() -# println("Building notebooks") -# hopts = HTMLOptions(; append_build_context=true) -# output_format = documenter_output -# bopts = BuildOptions(pluto_src_folder; output_format) -# build_notebooks(bopts, hopts) -# return nothing -# end - -# # Build the notebooks; defaults to true. -# if get(ENV, "BUILD_DOCS_NOTEBOOKS", "true") == "true" -# build() -# end - -# # Please do not use the same name as for a(n old) literate Tutorial -# pluto_files = [ -# "gnn_intro_pluto", -# "graph_classification_pluto", -# ] -# pluto_titles = [ -# "Intro to Graph Neural Networks ", -# "Graph Classification", -# ] - -# # build menu and write files myself - tp set edit url correctly. -# for (title, file) in zip(pluto_titles, pluto_files) -# global tutorial_menu -# rendered = build_notebooks( #though not really parallel here -# BuildOptions( -# pluto_src_folder; -# output_format=documenter_output, -# write_files=false, -# use_distributed=false, -# ), -# ["$(file).jl"], -# ) -# write( -# joinpath(pluto_output_folder, file * ".md"), -# """ -# ```@meta -# EditURL = "$(joinpath(pluto_src_folder, file * ".jl"))" -# ``` -# $(rendered[1]) -# """, -# ) -# push!(tutorial_menu, title => joinpath(pluto_relative_path, file * ".md")) -# end +# Build the notebooks; defaults to true. +if get(ENV, "BUILD_DOCS_NOTEBOOKS", "true") == "true" + build() +end DocMeta.setdocmeta!(GraphNeuralNetworks, :DocTestSetup, :(using GraphNeuralNetworks, Graphs, SparseArrays, NNlib, Flux); @@ -86,12 +45,11 @@ makedocs(; "Message Passing" => "messagepassing.md", "Model Building" => "models.md", "Datasets" => "datasets.md", - # "Tutorials" => tutorial_menu, - # "Tutorials" => - # [ - # "Intro to Graph Neural Networks" => "gnn_intro_pluto.md", - # "Graph Classification" => "graph_classification_pluto.md", - # ], + "Tutorials" => + [ + "Intro to Graph Neural Networks" => "tutorials/gnn_intro_pluto.md", + "Graph Classification" => "tutorials/graph_classification_pluto.md", + ], "API Reference" => [ "GNNGraph" => "api/gnngraph.md", diff --git a/docs/src/tutorials/gnn_intro_pluto.jl b/docs/src/tutorials/gnn_intro_pluto.jl index 233a92188..ce81d1f78 100644 --- a/docs/src/tutorials/gnn_intro_pluto.jl +++ b/docs/src/tutorials/gnn_intro_pluto.jl @@ -4,6 +4,23 @@ using Markdown using InteractiveUtils +# ╔═╡ 6f20e59c-b002-4d22-9ee0-b62596574776 +# ╠═╡ show_logs = false +begin + using Pkg + Pkg.activate(; temp=true) + packages = [ + PackageSpec(; path=joinpath(@__DIR__,"..","..","..")), + PackageSpec(; name="Flux", version="0.13"), + PackageSpec(; name="MLDatasets", version="0.7"), + PackageSpec(; name="GraphMakie"), + PackageSpec(; name="Graphs"), + PackageSpec(; name="CairoMakie"), + PackageSpec(; name="PlutoUI"), + ] + Pkg.add(packages) +end + # ╔═╡ 361e0948-d91a-11ec-2d95-2db77435a0c1 begin using Flux @@ -15,6 +32,7 @@ begin import CairoMakie as Makie using Graphs using PlutoUI + ENV["DATADEPS_ALWAYS_ACCEPT"] = "true" # don't ask for dataset download confirmation Random.seed!(17) # for reproducibility end; @@ -69,6 +87,7 @@ Now we convert the single-graph dataset to a `GNNGraph`. Moreover, we add a an a # ╔═╡ 8d41a9fa-eefe-40c9-8cc3-cd503cf7434d begin + # convert a MLDataset.jl's dataset to a GNNGraphs (or a collection of graphs) g = mldataset2gnngraph(dataset) x = zeros(Float32, g.num_nodes, g.num_nodes) @@ -142,7 +161,7 @@ Since a `GNNGraph` is an `AbstractGraph` from the `Graphs.jl` library, it suppor """ # ╔═╡ 9820cc77-ae0a-454a-86b6-a23dbc56b6fd -GraphMakie.graphplot(g, node_size=20, node_color=labels) +GraphMakie.graphplot(g |> to_unidirected, node_size=20, node_color=labels, arrow_show=false) # ╔═╡ 86135c51-950c-4c08-b9e0-6c892234ff87 md""" @@ -253,7 +272,7 @@ If you are not new to Flux, this scheme should appear familar to you. Note that our semi-supervised learning scenario is achieved by the following line: ``` -loss = criterion(out[:, train_mask], data.y[:, train_mask]) +loss = logitcrossentropy(ŷ[:,train_mask], y[:,train_mask]) ``` While we compute node embeddings for all of our nodes, we **only make use of the training nodes for computing the loss**. Here, this is implemented by filtering the output of the classifier `out` and ground-truth labels `data.y` to only contain the nodes in the `train_mask`. @@ -310,1747 +329,9 @@ As one can see, our 3-layer GCN model manages to linearly separating the communi Furthermore, we did this all with a few lines of code, thanks to the GraphNeuralNetworks.jl which helped us out with data handling and GNN implementations. 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