fix doctests

Author: CarloLucibello

GNNGraphs/docs/make.jl

@@ -25,7 +25,6 @@ mathengine = MathJax3(Dict(:loader => Dict("load" => ["[tex]/require", "[tex]/ma
 
 makedocs(;
     modules = [GNNGraphs],
-    doctest = false, # TODO enable doctest
     format = Documenter.HTML(; mathengine, 
                     prettyurls = get(ENV, "CI", nothing) == "true", 
                     assets = [],

GNNGraphs/docs/src/guides/heterograph.md

@@ -88,13 +88,11 @@ julia> g.etypes  # edge types
 
 Node, edge, and graph features can be added at construction time or later using:
 ```jldoctest hetero
-# equivalent to g.ndata[:user][:x] = ...
-julia> g[:user].x = rand(Float32, 64, 3);
+julia> g[:user].x = rand(Float32, 64, 3); # equivalent to g.ndata[:user][:x] = ...
 
 julia> g[:movie].z = rand(Float32, 64, 13);
 
-# equivalent to g.edata[(:user, :rate, :movie)][:e] = ...
-julia> g[:user, :rate, :movie].e = rand(Float32, 64, 4);
+julia> g[:user, :rate, :movie].e = rand(Float32, 64, 4); # equivalent to g.edata[(:user, :rate, :movie)][:e] = ...
 
 julia> g
 GNNHeteroGraph:
@@ -110,6 +108,8 @@ GNNHeteroGraph:
 ## Batching
 Similarly to graphs, also heterographs can be batched together.
 ```jldoctest hetero
+julia> using MLUtils
+
 julia> gs = [rand_bipartite_heterograph((5, 10), 20) for _ in 1:32];
 
 julia> MLUtils.batch(gs)
@@ -140,4 +140,4 @@ end
 
 ## Graph convolutions on heterographs
 
-See `HeteroGraphConv` for how to perform convolutions on heterogeneous graphs.
+See [`HeteroGraphConv`](@ref) for how to perform convolutions on heterogeneous graphs.

GNNGraphs/docs/src/guides/temporalgraph.md

@@ -65,6 +65,10 @@ Snapshots in a temporal graph can be accessed using indexing:
 julia> snapshots = [rand_graph(10, 20), rand_graph(10, 14), rand_graph(10, 22)];
 
 julia> tg = TemporalSnapshotsGNNGraph(snapshots)
+TemporalSnapshotsGNNGraph:
+  num_nodes: [10, 10, 10]
+  num_edges: [20, 14, 22]
+  num_snapshots: 3
 
 julia> tg[1] # first snapshot
 GNNGraph:
@@ -169,7 +173,7 @@ TemporalSnapshotsGNNGraph:
   num_edges: [20, 14, 22]
   num_snapshots: 3
   tgdata:
-        y = 3×1 Matrix{Float32}
+    y = 3×1 Matrix{Float32}
 
 julia> tg.ndata # vector of DataStore containing node features for each snapshot
 3-element Vector{DataStore}:

GNNGraphs/src/gnnheterograph/gnnheterograph.jl

@@ -198,18 +198,18 @@ function Base.show(io::IO, ::MIME"text/plain", g::GNNHeteroGraph)
             print(io, "\n  ndata:")
             for k in sort(collect(keys(g.ndata)))
                 isempty(g.ndata[k]) && continue    
-                print(io, "\n\t", _str(k), "  =>  $(shortsummary(g.ndata[k]))")
+                print(io, "\n    ", _str(k), "  =>  $(shortsummary(g.ndata[k]))")
             end
         end
         if !isempty(g.edata) && !all(isempty, values(g.edata))
             print(io, "\n  edata:")
             for k in sort(collect(keys(g.edata)))
                 isempty(g.edata[k]) && continue
-                print(io, "\n\t$k  =>  $(shortsummary(g.edata[k]))")
+                print(io, "\n    $k  =>  $(shortsummary(g.edata[k]))")
             end
         end
         if !isempty(g.gdata)
-            print(io, "\n  gdata:\n\t")
+            print(io, "\n  gdata:\n    ")
             shortsummary(io, g.gdata)
         end
     end

GNNGraphs/src/mldatasets.jl

@@ -15,11 +15,11 @@ GNNGraph:
   num_nodes: 2708
   num_edges: 10556
   ndata:
-        val_mask = 2708-element BitVector
-        targets = 2708-element Vector{Int64}
-        test_mask = 2708-element BitVector
-        features = 1433×2708 Matrix{Float32}
-        train_mask = 2708-element BitVector
+    val_mask = 2708-element BitVector
+    targets = 2708-element Vector{Int64}
+    test_mask = 2708-element BitVector
+    features = 1433×2708 Matrix{Float32}
+    train_mask = 2708-element BitVector
 ```
 """
 function mldataset2gnngraph(dataset::D) where {D}

GNNGraphs/src/transform.jl

@@ -459,14 +459,14 @@ GNNGraph:
   num_nodes: 4
   num_edges: 5
   edata:
-        e = 5-element Vector{Float64}
+    e = 5-element Vector{Float64}
 
 julia> g2 = to_bidirected(g)
 GNNGraph:
   num_nodes: 4
   num_edges: 7
   edata:
-        e = 7-element Vector{Float64}
+    e = 7-element Vector{Float64}
 
 julia> edge_index(g2)
 ([1, 2, 2, 3, 3, 4, 4], [2, 1, 3, 2, 4, 3, 4])
@@ -644,22 +644,22 @@ GNNGraph:
   num_nodes: 4
   num_edges: 4
   ndata:
-        x = 3×4 Matrix{Float32}
+    x = 3×4 Matrix{Float32}
 
 julia> g2 = rand_graph(5, 4, ndata=zeros(Float32, 3, 5))
 GNNGraph:
   num_nodes: 5
   num_edges: 4
   ndata:
-        x = 3×5 Matrix{Float32}
+    x = 3×5 Matrix{Float32}
 
 julia> g12 = MLUtils.batch([g1, g2])
 GNNGraph:
   num_nodes: 9
   num_edges: 8
   num_graphs: 2
   ndata:
-        x = 3×9 Matrix{Float32}
+    x = 3×9 Matrix{Float32}
 
 julia> g12.ndata.x
 3×9 Matrix{Float32}:

GNNLux/docs/make.jl

@@ -37,7 +37,6 @@ cp(joinpath(@__DIR__, "../../GNNlib/docs/src"),
 
 makedocs(;
     modules = [GNNLux, GNNGraphs, GNNlib],
-    doctest = false, # TODO: enable doctest
     plugins = [interlinks],
     format = Documenter.HTML(; mathengine, 
                     prettyurls = get(ENV, "CI", nothing) == "true", 

GNNlib/docs/make.jl

@@ -26,7 +26,6 @@ cp(joinpath(@__DIR__, "../../GNNGraphs/docs/src/"),
 
 makedocs(;
     modules = [GNNlib, GNNGraphs],
-    doctest = false, # TODO enable doctest
     plugins = [interlinks],
     format = Documenter.HTML(; mathengine, prettyurls, assets = assets, size_threshold=nothing),
     sitename = "GNNlib.jl",

GraphNeuralNetworks/docs/make.jl

@@ -37,7 +37,6 @@ cp(joinpath(@__DIR__, "../../GNNlib/docs/src"),
 
 makedocs(;
     modules = [GraphNeuralNetworks, GNNGraphs, GNNlib],
-    doctest = false, # TODO: enable doctest
     plugins = [interlinks],
     format = Documenter.HTML(; mathengine, 
                             prettyurls = get(ENV, "CI", nothing) == "true",