fix doctests

GNNGraphs/docs/make.jl

@@ -6,10 +6,10 @@ Pkg.instantiate()
 using Documenter
 using DocumenterInterLinks
 using GNNGraphs
-using MLUtils # this is needed by setdocmeta!
 import Graphs
 using Graphs: induced_subgraph
 
+ENV["DATADEPS_ALWAYS_ACCEPT"] = true # for MLDatasets
 
 DocMeta.setdocmeta!(GNNGraphs, :DocTestSetup, :(using GNNGraphs, MLUtils); recursive = true)
 
@@ -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/api/heterograph.md

@@ -38,3 +38,4 @@ Modules = [GNNGraphs]
 Pages   = ["gnnheterograph/generate.jl"]
 Private = false
 ```
+å

GNNGraphs/docs/src/guides/heterograph.md

@@ -88,28 +88,28 @@ 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:
   num_nodes: Dict(:movie => 13, :user => 3)
   num_edges: Dict((:user, :rate, :movie) => 4)
   ndata:
-        :movie  =>  DataStore(z = [64×13 Matrix{Float32}])
-        :user  =>  DataStore(x = [64×3 Matrix{Float32}])
+    :movie  =>  DataStore(z = [64×13 Matrix{Float32}])
+    :user  =>  DataStore(x = [64×3 Matrix{Float32}])
   edata:
-        (:user, :rate, :movie)  =>  DataStore(e = [64×4 Matrix{Float32}])
+    (:user, :rate, :movie)  =>  DataStore(e = [64×4 Matrix{Float32}])
 ```
 
 ## 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)

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/Project.toml

@@ -11,6 +11,7 @@ Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 TSne = "24678dba-d5e9-5843-a4c6-250288b04835"

GNNLux/docs/make.jl

@@ -12,6 +12,10 @@ using GNNLux
 using Lux, GNNGraphs, GNNlib, Graphs
 using DocumenterInterLinks
 
+ENV["DATADEPS_ALWAYS_ACCEPT"] = true # for MLDatasets
+
+DocMeta.setdocmeta!(GNNGraphs, :DocTestSetup, :(using GNNGraphs, MLUtils); recursive = true)
+DocMeta.setdocmeta!(GNNlib, :DocTestSetup, :(using GNNlib); recursive = true)
 DocMeta.setdocmeta!(GNNLux, :DocTestSetup, :(using GNNLux); recursive = true)
 
 mathengine = MathJax3(Dict(:loader => Dict("load" => ["[tex]/require", "[tex]/mathtools"]),
@@ -37,7 +41,6 @@ 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", 
@@ -82,7 +85,7 @@ makedocs(;
             "Layers" => [
                 "Basic layers" => "api/basic.md",
                 "Convolutional layers" => "api/conv.md",
-                # "Pooling layers" => "api/pool.md",
+                "Pooling layers" => "api/pool.md",
                 "Temporal Convolutional layers" => "api/temporalconv.md",
                 # "Hetero Convolutional layers" => "api/heteroconv.md",
             ]

GNNLux/docs/src/api/pool.md

@@ -5,13 +5,6 @@ CollapsedDocStrings = true
 
 # Pooling Layers
 
-## Index
-
-```@index
-Order = [:type, :function]
-Pages   = ["pool.md"]
-```
-
 ```@autodocs
 Modules = [GNNLux]
 Pages   = ["layers/pool.jl"]

GNNLux/src/layers/basic.jl

@@ -31,9 +31,14 @@ julia> using Lux, GNNLux, Random
 
 julia> rng = Random.default_rng();
 
-julia> m = GNNChain(GCNConv(2=>5), 
-                    x -> relu.(x), 
-                    Dense(5=>4))
+julia> m = GNNChain(GCNConv(2 => 5, relu), Dense(5 => 4))
+GNNChain(
+    layers = NamedTuple(
+        layer_1 = GCNConv(2 => 5, relu),  # 15 parameters
+        layer_2 = Dense(5 => 4),        # 24 parameters
+    ),
+)         # Total: 39 parameters,
+          #        plus 0 states.
 
 julia> x = randn(rng, Float32, 2, 3);
 
@@ -44,8 +49,10 @@ GNNGraph:
 
 julia> ps, st = LuxCore.setup(rng, m);
 
-julia> m(g, x, ps, st)     # First entry is the output, second entry is the state of the model
-(Float32[-0.15594329 -0.15594329 -0.15594329; 0.93431795 0.93431795 0.93431795; 0.27568763 0.27568763 0.27568763; 0.12568939 0.12568939 0.12568939], (layer_1 = NamedTuple(), layer_2 = NamedTuple(), layer_3 = NamedTuple()))
+julia> y, st = m(g, x, ps, st);     # First entry is the output, second entry is the state of the model
+
+julia> size(y)
+(4, 3)
 ```
 """
 @concrete struct GNNChain <: GNNContainerLayer{(:layers,)}

GNNlib/docs/Project.toml

@@ -4,3 +4,5 @@ DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
 GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
 GNNlib = "a6a84749-d869-43f8-aacc-be26a1996e48"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"

GNNlib/docs/make.jl

@@ -12,6 +12,10 @@ using GNNGraphs
 import Graphs
 using DocumenterInterLinks
 
+ENV["DATADEPS_ALWAYS_ACCEPT"] = true # for MLDatasets
+DocMeta.setdocmeta!(GNNGraphs, :DocTestSetup, :(using GNNGraphs, MLUtils); recursive = true)
+DocMeta.setdocmeta!(GNNlib, :DocTestSetup, :(using GNNlib); recursive = true)
+
 assets=[]
 prettyurls = get(ENV, "CI", nothing) == "true"
 mathengine = MathJax3()
@@ -26,7 +30,6 @@ 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/Project.toml

@@ -12,6 +12,7 @@ Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 PlutoStaticHTML = "359b1769-a58e-495b-9770-312e911026ad"
 PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"

GraphNeuralNetworks/docs/make.jl

@@ -12,6 +12,10 @@ using GraphNeuralNetworks
 using Flux, GNNGraphs, GNNlib, Graphs
 using DocumenterInterLinks
 
+ENV["DATADEPS_ALWAYS_ACCEPT"] = true # for MLDatasets
+
+DocMeta.setdocmeta!(GNNGraphs, :DocTestSetup, :(using GNNGraphs, MLUtils); recursive = true)
+DocMeta.setdocmeta!(GNNlib, :DocTestSetup, :(using GNNlib); recursive = true)
 DocMeta.setdocmeta!(GraphNeuralNetworks, :DocTestSetup, :(using GraphNeuralNetworks); recursive = true)
 
 mathengine = MathJax3(Dict(:loader => Dict("load" => ["[tex]/require", "[tex]/mathtools"]),
@@ -37,7 +41,6 @@ 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", 

GraphNeuralNetworks/docs/src/api/pool.md

@@ -5,13 +5,6 @@ CollapsedDocStrings = true
 
 # Pooling Layers
 
-## Index
-
-```@index
-Order = [:type, :function]
-Pages   = ["pool.md"]
-```
-
 ```@autodocs
 Modules = [GraphNeuralNetworks]
 Pages   = ["layers/pool.jl"]

GraphNeuralNetworks/src/layers/basic.jl

@@ -74,30 +74,22 @@ julia> using Flux, GraphNeuralNetworks
 julia> m = GNNChain(GCNConv(2=>5), 
                     BatchNorm(5), 
                     x -> relu.(x), 
-                    Dense(5, 4))
-GNNChain(GCNConv(2 => 5), BatchNorm(5), #7, Dense(5 => 4))
+                    Dense(5, 4));
 
 julia> x = randn(Float32, 2, 3);
 
 julia> g = rand_graph(3, 6)
 GNNGraph:
-    num_nodes = 3
-    num_edges = 6
+  num_nodes: 3
+  num_edges: 6
 
-julia> m(g, x)
-4×3 Matrix{Float32}:
-    -0.795592  -0.795592  -0.795592
-    -0.736409  -0.736409  -0.736409
-    0.994925   0.994925   0.994925
-    0.857549   0.857549   0.857549
+julia> m(g, x) |> size
+(4, 3)
 
-julia> m2 = GNNChain(enc = m, 
-                     dec = DotDecoder())
-GNNChain(enc = GNNChain(GCNConv(2 => 5), BatchNorm(5), #7, Dense(5 => 4)), dec = DotDecoder())
+julia> m2 = GNNChain(enc = m, dec = DotDecoder());
 
-julia> m2(g, x)
-1×6 Matrix{Float32}:
- 2.90053  2.90053  2.90053  2.90053  2.90053  2.90053
+julia> m2(g, x) |> size
+(1, 6)
 
 julia> m2[:enc](g, x) == m(g, x)
 true
@@ -196,15 +188,14 @@ returns the dot product `x_i ⋅ xj` on each edge.
 ```jldoctest
 julia> g = rand_graph(5, 6)
 GNNGraph:
-    num_nodes = 5
-    num_edges = 6
+  num_nodes: 5
+  num_edges: 6
 
 julia> dotdec = DotDecoder()
 DotDecoder()
 
-julia> dotdec(g, rand(2, 5))
-1×6 Matrix{Float64}:
- 0.345098  0.458305  0.106353  0.345098  0.458305  0.106353
+julia> dotdec(g, rand(2, 5)) |> size
+(1, 6)
 ```
 """
 struct DotDecoder <: GNNLayer end

GraphNeuralNetworks/src/layers/heteroconv.jl

@@ -21,10 +21,12 @@ have to be aggregated using the `aggr` function. The default is to sum the outpu
 # Examples 
 
 ```jldoctest
-julia> g = rand_bipartite_heterograph((10, 15), 20)
+julia> using GraphNeuralNetworks, Flux
+
+julia> g = rand_bipartite_heterograph((10, 15), 80)
 GNNHeteroGraph:
   num_nodes: Dict(:A => 10, :B => 15)
-  num_edges: Dict((:A, :to, :B) => 20, (:B, :to, :A) => 20)
+  num_edges: Dict((:A, :to, :B) => 80, (:B, :to, :A) => 80)
 
 julia> x = (A = rand(Float32, 64, 10), B = rand(Float32, 64, 15));