polishing all

Author: CarloLucibello

docs/Project.toml

@@ -1,16 +1,13 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-GraphMakie = "1ecd5474-83a3-4783-bb4f-06765db800d2"
 GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+MarkdownLiteral = "736d6165-7244-6769-4267-6b50796e6954"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
 PlutoStaticHTML = "359b1769-a58e-495b-9770-312e911026ad"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008"

docs/make.jl

@@ -1,23 +1,44 @@
 using Flux, NNlib, GraphNeuralNetworks, Graphs, SparseArrays
 using Documenter
+
+# See this for a template of Pluto+Documenter 
+# https://github.com/rikhuijzer/PlutoStaticHTML.jl/blob/main/docs/make.jl
 using Pluto, PlutoStaticHTML
+using Documenter: MathJax3
 
-TutorialMenu = Array{Pair{String,String},1}()
+tutorial_menu = Array{Pair{String,String},1}()
 
 #
 # Generate Pluto Tutorial HTMLs
 
 # First tutorial with AD
-pluto_src_folder = joinpath(@__DIR__, "tutorials/")
-pluto_output_folder = joinpath(@__DIR__, "tutorials/")
+pluto_src_folder = joinpath(@__DIR__, "src", "tutorials")
+pluto_output_folder = joinpath(@__DIR__, "src", "tutorials")
 pluto_relative_path = "tutorials/"
 mkpath(pluto_output_folder)
-#
-#
+
+# """
+#     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",
+    "gnn_intro_pluto",
+    "graph_classification_pluto",
 ]
 pluto_titles = [
     "Intro to Graph Neural Networks ",
@@ -26,7 +47,7 @@ pluto_titles = [
 
 # build menu and write files myself - tp set edit url correctly.
 for (title, file) in zip(pluto_titles, pluto_files)
-    global TutorialMenu
+    global tutorial_menu
     rendered = build_notebooks( #though not really parallel here
         BuildOptions(
             pluto_src_folder;
@@ -37,30 +58,41 @@ for (title, file) in zip(pluto_titles, pluto_files)
         ["$(file).jl"],
     )
     write(
-        pluto_output_folder * file * ".md",
+        joinpath(pluto_output_folder, file * ".md"),
         """
         ```@meta
-        EditURL = "$(pluto_src_folder)$(file).jl"
+        EditURL = "$(joinpath(pluto_src_folder, file * ".jl"))"
         ```
         $(rendered[1])
         """,
     )
-    push!(TutorialMenu, title => joinpath(pluto_relative_path, file * ".md"))
+    push!(tutorial_menu, title => joinpath(pluto_relative_path, file * ".md"))
 end
 
 DocMeta.setdocmeta!(GraphNeuralNetworks, :DocTestSetup, 
     :(using GraphNeuralNetworks, Graphs, SparseArrays, NNlib, Flux); 
     recursive=true)
 
+prettyurls = get(ENV, "CI", nothing) == "true"
+mathengine = MathJax3()
+    
 makedocs(;
-    modules=[GraphNeuralNetworks, NNlib, Flux, Graphs, SparseArrays],
-    doctest=false, clean=true,     
+    modules = [GraphNeuralNetworks, NNlib, Flux, Graphs, SparseArrays],
+    doctest = false, 
+    clean = true,    
+    format= Documenter.HTML(; mathengine, prettyurls),
     sitename = "GraphNeuralNetworks.jl",
     pages = ["Home" => "index.md",
              "Graphs" => "gnngraph.md",
              "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",
+            #     ],
              "API Reference" =>
                [
                 "GNNGraph" => "api/gnngraph.md",

docs/src/datasets.md

@@ -2,8 +2,8 @@
 
 GraphNeuralNetworks.jl doesn't come with its own datasets, but leverages those available in the Julia (and non-Julia) ecosystem. In particular, the [examples in the GraphNeuralNetworks.jl repository](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) make use of the [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl) package. There you will find common graph datasets such as Cora, PubMed, Citeseer, TUDataset and [many others](https://juliaml.github.io/MLDatasets.jl/dev/datasets/graphs/).
 
-GraphNeuralNetworks.jl provides the [`mldatasets2gnngraph`](@ref) method for interfacing with MLDatasets.jl.
+GraphNeuralNetworks.jl provides the [`mldataset2gnngraph`](@ref) method for interfacing with MLDatasets.jl.
 
 ```@docs
-mldatasets2gnngraph
+mldataset2gnngraph
 ```

docs/src/tutorials/gnn_intro.pluto.jl renamed to docs/src/tutorials/gnn_intro_pluto.jl

@@ -14,30 +14,33 @@ begin
 	import GraphMakie
 	import CairoMakie as Makie
 	using Graphs
-end
-
-# ╔═╡ 5de79e7a-46e6-4cbc-9cf4-0e21823fd9ed
-## These Pluto noteboook is a julia adaptation of the
-## Pytorch Geometric tutorials that can be found at  
-## https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html
+	using PlutoUI
+	Random.seed!(17); # for reproducibility
+end;
 
 # ╔═╡ 03a9e023-e682-4ea3-a10b-14c4d101b291
-
 md"""
 # Introduction: Hands-on Graph Neural Networks
 
+*This Pluto noteboook is a julia adaptation of the Pytorch Geometric tutorials that can be found [here](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html).*
+
 Recently, deep learning on graphs has emerged to one of the hottest research fields in the deep learning community.
 Here, **Graph Neural Networks (GNNs)** aim to generalize classical deep learning concepts to irregular structured data (in contrast to images or texts) and to enable neural networks to reason about objects and their relations.
 
 This is done by following a simple **neural message passing scheme**, where node features ``\mathbf{x}_i^{(\ell)}`` of all nodes ``i \in \mathcal{V}`` in a graph ``\mathcal{G} = (\mathcal{V}, \mathcal{E})`` are iteratively updated by aggregating localized information from their neighbors ``\mathcal{N}(i)``:
 
 ```math
-\mathbf{x}_i^{(\ell + 1)} = f^{(\ell + 1)}_{\theta} \left( \mathbf{x}_i^{(\ell)}, \left\{ \mathbf{x}_w^{(\ell)} : w \in \mathcal{N}(i) \right\} \right)
+\mathbf{x}_i^{(\ell + 1)} = f^{(\ell + 1)}_{\theta} \left( \mathbf{x}_i^{(\ell)}, \left\{ \mathbf{x}_j^{(\ell)} : j \in \mathcal{N}(i) \right\} \right)
 ```
 
 This tutorial will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the **[GraphNeuralNetworks.jl library](https://github.com/CarloLucibello/GraphNeuralNetworks.jl)**.
 GNN.jl is an extension library to the popular deep learning framework [Flux.jl](https://fluxml.ai/Flux.jl/stable/), and consists of various methods and utilities to ease the implementation of Graph Neural Networks.
 
+Let's first import the packages we need:
+"""
+
+# ╔═╡ ef96f5ae-724d-4b8e-b7d7-c116ad1c3279
+md"""
 Following [Kipf et al. (2017)](https://arxiv.org/abs/1609.02907), let's dive into the world of GNNs by looking at a simple graph-structured example, the well-known [**Zachary's karate club network**](https://en.wikipedia.org/wiki/Zachary%27s_karate_club). This graph describes a social network of 34 members of a karate club and documents links between members who interacted outside the club. Here, we are interested in detecting communities that arise from the member's interaction.
 
 GNN.jl provides utilities to convert [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl)'s datasets to its own type:
@@ -88,7 +91,7 @@ Let's now look at the underlying graph in more detail:
 """
 
 # ╔═╡ a7ad9de3-3e18-4aff-b118-a4d798a2f4ec
-begin
+with_terminal() do
 	# Gather some statistics about the graph.
 	println("Number of nodes: $(g.num_nodes)")
 	println("Number of edges: $(g.num_edges)")
@@ -139,13 +142,7 @@ Since a `GNNGraph` is an `AbstractGraph` from the `Graphs.jl` library, it suppor
 """
 
 # ╔═╡ 9820cc77-ae0a-454a-86b6-a23dbc56b6fd
-begin
-	g2= rand_graph(g.num_nodes, 30, bidirected=false)
-	GraphMakie.graphplot(Graph(g2), node_size=20, node_color=labels, arrow_show=false) 
-end
-
-# ╔═╡ cf428e12-17fc-46c9-b0ec-906a79f001cb
-?GraphMakie.graphplot
+GraphMakie.graphplot(g, node_size=20, node_color=labels) 
 
 # ╔═╡ 86135c51-950c-4c08-b9e0-6c892234ff87
 md"""
@@ -323,6 +320,7 @@ GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
@@ -333,6 +331,7 @@ GraphMakie = "~0.3.5"
 GraphNeuralNetworks = "~0.4.2"
 Graphs = "~1.7.0"
 MLDatasets = "~0.7.1"
+PlutoUI = "~0.7.39"
 """
 
 # ╔═╡ 00000000-0000-0000-0000-000000000002
@@ -348,6 +347,12 @@ git-tree-sha1 = "6f1d9bc1c08f9f4a8fa92e3ea3cb50153a1b40d4"
 uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
 version = "1.1.0"
 
+[[deps.AbstractPlutoDingetjes]]
+deps = ["Pkg"]
+git-tree-sha1 = "8eaf9f1b4921132a4cff3f36a1d9ba923b14a481"
+uuid = "6e696c72-6542-2067-7265-42206c756150"
+version = "1.1.4"
+
 [[deps.AbstractTrees]]
 git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5"
 uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -931,6 +936,24 @@ git-tree-sha1 = "cb7099a0109939f16a4d3b572ba8396b1f6c7c31"
 uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a"
 version = "0.3.10"
 
+[[deps.Hyperscript]]
+deps = ["Test"]
+git-tree-sha1 = "8d511d5b81240fc8e6802386302675bdf47737b9"
+uuid = "47d2ed2b-36de-50cf-bf87-49c2cf4b8b91"
+version = "0.0.4"
+
+[[deps.HypertextLiteral]]
+deps = ["Tricks"]
+git-tree-sha1 = "c47c5fa4c5308f27ccaac35504858d8914e102f9"
+uuid = "ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2"
+version = "0.9.4"
+
+[[deps.IOCapture]]
+deps = ["Logging", "Random"]
+git-tree-sha1 = "f7be53659ab06ddc986428d3a9dcc95f6fa6705a"
+uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89"
+version = "0.2.2"
+
 [[deps.IRTools]]
 deps = ["InteractiveUtils", "MacroTools", "Test"]
 git-tree-sha1 = "af14a478780ca78d5eb9908b263023096c2b9d64"
@@ -1505,6 +1528,12 @@ git-tree-sha1 = "bb16469fd5224100e422f0b027d26c5a25de1200"
 uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043"
 version = "1.2.0"
 
+[[deps.PlutoUI]]
+deps = ["AbstractPlutoDingetjes", "Base64", "ColorTypes", "Dates", "Hyperscript", "HypertextLiteral", "IOCapture", "InteractiveUtils", "JSON", "Logging", "Markdown", "Random", "Reexport", "UUIDs"]
+git-tree-sha1 = "8d1f54886b9037091edf146b517989fc4a09efec"
+uuid = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
+version = "0.7.39"
+
 [[deps.PolygonOps]]
 git-tree-sha1 = "77b3d3605fc1cd0b42d95eba87dfcd2bf67d5ff6"
 uuid = "647866c9-e3ac-4575-94e7-e3d426903924"
@@ -1837,6 +1866,11 @@ git-tree-sha1 = "c76399a3bbe6f5a88faa33c8f8a65aa631d95013"
 uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
 version = "0.4.73"
 
+[[deps.Tricks]]
+git-tree-sha1 = "6bac775f2d42a611cdfcd1fb217ee719630c4175"
+uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
+version = "0.1.6"
+
 [[deps.TupleTools]]
 git-tree-sha1 = "3c712976c47707ff893cf6ba4354aa14db1d8938"
 uuid = "9d95972d-f1c8-5527-a6e0-b4b365fa01f6"
@@ -2016,9 +2050,9 @@ version = "3.5.0+0"
 """
 
 # ╔═╡ Cell order:
-# ╠═361e0948-d91a-11ec-2d95-2db77435a0c1
-# ╠═5de79e7a-46e6-4cbc-9cf4-0e21823fd9ed
 # ╟─03a9e023-e682-4ea3-a10b-14c4d101b291
+# ╠═361e0948-d91a-11ec-2d95-2db77435a0c1
+# ╟─ef96f5ae-724d-4b8e-b7d7-c116ad1c3279
 # ╠═4ba372d4-7a6a-41e0-92a0-9547a78e2898
 # ╟─55aca2f0-4bbb-4d3a-9777-703896cfc548
 # ╠═a1d35896-0f52-4c8b-b7dc-ec65649237c8
@@ -2031,7 +2065,6 @@ version = "3.5.0+0"
 # ╠═d627736a-fd5a-4cdc-bd4e-89ff8b8c55bd
 # ╟─98bb86d2-a7b9-4110-8851-8829a9f9b4d0
 # ╠═9820cc77-ae0a-454a-86b6-a23dbc56b6fd
-# ╠═cf428e12-17fc-46c9-b0ec-906a79f001cb
 # ╟─86135c51-950c-4c08-b9e0-6c892234ff87
 # ╠═88d1e59f-73d6-46ee-87e8-35beb7bc7674
 # ╟─9838189c-5cf6-4f21-b58e-3bb905408ad3