fix many gnngraph doctests

GNNGraphs/docs/make.jl

@@ -1,6 +1,7 @@
 using Documenter
 using DocumenterInterLinks
 using GNNGraphs
+using MLUtils # this is needed by setdocmeta!
 import Graphs
 using Graphs: induced_subgraph
 

GNNGraphs/docs/src/guides/heterograph.md

@@ -1,3 +1,7 @@
+```@meta
+CurrentModule = GNNGraphs
+```
+
 # Heterogeneous Graphs
 
 Heterogeneous graphs (also called heterographs), are graphs where each node has a type,
@@ -67,18 +71,15 @@ julia> g.num_edges
 Dict{Tuple{Symbol, Symbol, Symbol}, Int64} with 1 entry:
   (:user, :rate, :movie) => 4
 
-# source and target node for a given relation
-julia> edge_index(g, (:user, :rate, :movie))
+julia> edge_index(g, (:user, :rate, :movie)) # source and target node for a given relation
 ([1, 1, 2, 3], [7, 13, 5, 7])
 
-# node types
-julia> g.ntypes
+julia> g.ntypes  # node types
 2-element Vector{Symbol}:
  :user
  :movie
 
-# edge types
-julia> g.etypes
+julia> g.etypes  # edge types
 1-element Vector{Tuple{Symbol, Symbol, Symbol}}:
  (:user, :rate, :movie)
 ```
@@ -120,8 +121,8 @@ GNNHeteroGraph:
 Batching is automatically performed by the [`DataLoader`](https://fluxml.ai/Flux.jl/stable/data/mlutils/#MLUtils.DataLoader) iterator
 when the `collate` option is set to `true`.
 
-```jldoctest hetero
-using Flux: DataLoader
+```julia
+using MLUtils: DataLoader
 
 data = [rand_bipartite_heterograph((5, 10), 20, 
             ndata=Dict(:A=>rand(Float32, 3, 5))) 

GNNGraphs/docs/src/guides/temporalgraph.md

@@ -1,3 +1,7 @@
+```@meta
+CurrentModule = GNNGraphs
+```
+
 # Temporal Graphs
 
 Temporal Graphs are graphs with time varying topologies and  features. In GNNGraphs.jl, temporal graphs with fixed number of nodes over time are supported by the [`TemporalSnapshotsGNNGraph`](@ref) type.
@@ -45,7 +49,7 @@ TemporalSnapshotsGNNGraph:
 
 See [`rand_temporal_radius_graph`](@ref) and [`rand_temporal_hyperbolic_graph`](@ref) for generating random temporal graphs. 
 
-```jldoctest temporal
+```julia
 julia> tg = rand_temporal_radius_graph(10, 3, 0.1, 0.5)
 TemporalSnapshotsGNNGraph:
   num_nodes: [10, 10, 10]
@@ -97,28 +101,30 @@ A temporal graph can store global feature for the entire time series in the `tgd
 Also, each snapshot can store node, edge, and graph features in the `ndata`, `edata`, and `gdata` fields, respectively. 
 
 ```jldoctest temporal
-julia> snapshots = [rand_graph(10,20; ndata = rand(3,10)), rand_graph(10,14; ndata = rand(4,10)), rand_graph(10,22; ndata = rand(5,10))]; # node features at construction time
+julia> snapshots = [rand_graph(10, 20; ndata = rand(Float32, 3, 10)), 
+                    rand_graph(10, 14; ndata = rand(Float32, 4, 10)), 
+                    rand_graph(10, 22; ndata = rand(Float32, 5, 10))]; # node features at construction time
 
 julia> tg = TemporalSnapshotsGNNGraph(snapshots);
 
-julia> tg.tgdata.y = rand(3,1); # add global features after construction
+julia> tg.tgdata.y = rand(Float32, 3, 1); # add global features after construction
 
 julia> tg
 TemporalSnapshotsGNNGraph:
   num_nodes: [10, 10, 10]
   num_edges: [20, 14, 22]
   num_snapshots: 3
   tgdata:
-        y = 3×1 Matrix{Float64}
+        y = 3×1 Matrix{Float32}
 
 julia> tg.ndata # vector of DataStore containing node features for each snapshot
 3-element Vector{DataStore}:
  DataStore(10) with 1 element:
-  x = 3×10 Matrix{Float64}
+  x = 3×10 Matrix{Float32}
  DataStore(10) with 1 element:
-  x = 4×10 Matrix{Float64}
+  x = 4×10 Matrix{Float32}
  DataStore(10) with 1 element:
-  x = 5×10 Matrix{Float64}
+  x = 5×10 Matrix{Float32}
 
 julia> [ds.x for ds in tg.ndata]; # vector containing the x feature of each snapshot
 

GNNGraphs/src/GNNGraphs.jl

@@ -4,7 +4,7 @@ using SparseArrays
 using Functors: @functor
 import Graphs
 using Graphs: AbstractGraph, outneighbors, inneighbors, adjacency_matrix, degree, 
-              has_self_loops, is_directed, induced_subgraph
+              has_self_loops, is_directed, induced_subgraph, has_edge
 import NearestNeighbors
 import NNlib
 import StatsBase
@@ -58,8 +58,9 @@ export adjacency_list,
 # from Graphs
        adjacency_matrix,
        degree,
-       has_self_loops,
+       has_edge, 
        has_isolated_nodes,
+       has_self_loops,
        inneighbors,
        outneighbors,
        khop_adj

GNNGraphs/src/datastore.jl

@@ -15,29 +15,12 @@ DataStore(3) with 2 elements:
   x = 2×3 Matrix{Float32}
 
 julia> ds = DataStore(3, Dict(:x => rand(Float32, 2, 3), :y => rand(Float32, 3))); # equivalent to above
-
-julia> ds = DataStore(3, (x = rand(Float32, 2, 3), y = rand(Float32, 30)))
-ERROR: AssertionError: DataStore: data[y] has 30 observations, but n = 3
-Stacktrace:
- [1] DataStore(n::Int64, data::Dict{Symbol, Any})
-   @ GNNGraphs ~/.julia/dev/GNNGraphs/datastore.jl:54
- [2] DataStore(n::Int64, data::NamedTuple{(:x, :y), Tuple{Matrix{Float32}, Vector{Float32}}})
-   @ GNNGraphs ~/.julia/dev/GNNGraphs/datastore.jl:73
- [3] top-level scope
-   @ REPL[13]:1
-
-julia> ds = DataStore(x = randFloat32, 2, 3), y = rand(Float32, 30)) # no checks
-DataStore() with 2 elements:
-  y = 30-element Vector{Float32}
-  x = 2×3 Matrix{Float32}
-  y = 30-element Vector{Float64}
-  x = 2×3 Matrix{Float64}
 ```
 
 The `DataStore` has an interface similar to both dictionaries and named tuples.
 Arrays can be accessed and added using either the indexing or the property syntax:
 
-```jldoctest datastore
+```jldoctest docstr_datastore
 julia> ds = DataStore(x = ones(Float32, 2, 3), y = zeros(Float32, 3))
 DataStore() with 2 elements:
   y = 3-element Vector{Float32}
@@ -59,14 +42,16 @@ The `DataStore` can be iterated over, and the keys and values can be accessed
 using `keys(ds)` and `values(ds)`. `map(f, ds)` applies the function `f`
 to each feature array:
 
-```jldoctest datastore
+```jldoctest docstr_datastore
 julia> ds2 = map(x -> x .+ 1, ds)
-DataStore() with 2 elements:
-  a = 2-element Vector{Float64}
-  b = 2-element Vector{Float64}
+DataStore() with 3 elements:
+  y = 3-element Vector{Float32}
+  z = 3-element Vector{Float32}
+  x = 2×3 Matrix{Float32}
 
-julia> ds2.a
-2-element Vector{Float64}:
+julia> ds2.z
+3-element Vector{Float32}:
+ 1.0
  1.0
  1.0
 ```

GNNGraphs/src/generate.jl

@@ -16,26 +16,26 @@ Additional keyword arguments will be passed to the [`GNNGraph`](@ref) constructo
 
 # Examples
 
-```jldoctest
+```julia
 julia> g = rand_graph(5, 4, bidirected=false)
 GNNGraph:
-    num_nodes = 5
-    num_edges = 4
+  num_nodes: 5
+  num_edges: 4
 
 julia> edge_index(g)
-([1, 3, 3, 4], [5, 4, 5, 2])
+([4, 3, 2, 1], [5, 4, 3, 2])
 
 # In the bidirected case, edge data will be duplicated on the reverse edges if needed.
 julia> g = rand_graph(5, 4, edata=rand(Float32, 16, 2))
 GNNGraph:
-    num_nodes = 5
-    num_edges = 4
-    edata:
-        e => (16, 4)
+  num_nodes: 5
+  num_edges: 4
+  edata:
+        e = 16×4 Matrix{Float32}
 
 # Each edge has a reverse
 julia> edge_index(g)
-([1, 3, 3, 4], [3, 4, 1, 3])
+([1, 1, 5, 3], [5, 3, 1, 1])
 ```
 """
 function rand_graph(n::Integer, m::Integer; seed=-1, kws...)
@@ -85,8 +85,8 @@ Additional keyword arguments will be passed to the [`GNNHeteroGraph`](@ref) cons
 julia> g = rand_heterograph((:user => 10, :movie => 20),
                             (:user, :rate, :movie) => 30)
 GNNHeteroGraph:
-  num_nodes: (:user => 10, :movie => 20)         
-  num_edges: ((:user, :rate, :movie) => 30,)
+  num_nodes: Dict(:movie => 20, :user => 10)         
+  num_edges: Dict((:user, :rate, :movie) => 30)
 ```
 """
 function rand_heterograph end
@@ -161,7 +161,7 @@ See [`rand_heterograph`](@ref) for a more general version.
 
 # Examples
 
-```julia-repl
+```julia
 julia> g = rand_bipartite_heterograph((10, 15), 20)
 GNNHeteroGraph:
   num_nodes: (:A => 10, :B => 15)
@@ -214,7 +214,7 @@ to its `k` closest `points`.
 
 # Examples
 
-```jldoctest
+```julia
 julia> n, k = 10, 3;
 
 julia> x = rand(Float32, 3, n);
@@ -231,7 +231,6 @@ GNNGraph:
     num_nodes = 10
     num_edges = 30
     num_graphs = 2
-
 ```
 """
 function knn_graph(points::AbstractMatrix, k::Int;
@@ -295,7 +294,7 @@ to its neighbors within a given distance `r`.
 
 # Examples
 
-```jldoctest
+```julia
 julia> n, r = 10, 0.75;
 
 julia> x = rand(Float32, 3, n);
@@ -312,9 +311,10 @@ GNNGraph:
     num_nodes = 10
     num_edges = 20
     num_graphs = 2
-
 ```
+
 # References
+
 Section B paragraphs 1 and 2 of the paper [Dynamic Hidden-Variable Network Models](https://arxiv.org/pdf/2101.00414.pdf)
 """
 function radius_graph(points::AbstractMatrix, r::AbstractFloat;
@@ -447,7 +447,7 @@ First, the positions of the nodes are generated with a quasi-uniform distributio
 
 # Example
 
-```jldoctest
+```julia
 julia> n, snaps, α, R, speed, ζ = 10, 5, 1.0, 4.0, 0.1, 1.0;
 
 julia> thg = rand_temporal_hyperbolic_graph(n, snaps; α, R, speed, ζ)

GNNGraphs/src/mldatasets.jl

@@ -12,14 +12,14 @@ julia> using MLDatasets, GNNGraphs
 
 julia> mldataset2gnngraph(Cora())
 GNNGraph:
-    num_nodes = 2708
-    num_edges = 10556
-    ndata:
-        features => 1433×2708 Matrix{Float32}
-        targets => 2708-element Vector{Int64}
-        train_mask => 2708-element BitVector
-        val_mask => 2708-element BitVector
-        test_mask => 2708-element BitVector
+  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
 ```
 """
 function mldataset2gnngraph(dataset::D) where {D}

GNNGraphs/src/query.jl

@@ -65,8 +65,8 @@ Return `true` if there is an edge of type `edge_t` from node `i` to node `j` in
 ```jldoctest
 julia> g = rand_bipartite_heterograph((2, 2), (4, 0), bidirected=false)
 GNNHeteroGraph:
-  num_nodes: (:A => 2, :B => 2)
-  num_edges: ((:A, :to, :B) => 4, (:B, :to, :A) => 0)
+  num_nodes: Dict(:A => 2, :B => 2)
+  num_edges: Dict((:A, :to, :B) => 4, (:B, :to, :A) => 0)
 
 julia> has_edge(g, (:A,:to,:B), 1, 1)
 true

GNNGraphs/src/samplers.jl

@@ -12,11 +12,13 @@ originally introduced in ["Inductive Representation Learning on Large Graphs"}(h
 - `num_layers::Int`: The number of layers for neighborhood expansion (how far to sample neighbors).
 - `batch_size::Union{Int, Nothing}`: The size of the batch. If not specified, it defaults to the number of `input_nodes`.
 
-# Usage
-```jldoctest
-julia>  loader = NeighborLoader(graph; num_neighbors=[10, 5], input_nodes=[1, 2, 3], num_layers=2)
+# Examples
+
+```julia
+julia> loader = NeighborLoader(graph; num_neighbors=[10, 5], input_nodes=[1, 2, 3], num_layers=2)
 
 julia> batch_counter = 0
+
 julia> for mini_batch_gnn in loader
             batch_counter += 1
             println("Batch ", batch_counter, ": Nodes in mini-batch graph: ", nv(mini_batch_gnn))