fix

Author: CarloLucibello

GNNGraphs/test/mldatasets.jl

@@ -1,5 +1,5 @@
 @testitem "mldataset2gnngraph" begin
-    using MLDataset
+    using MLDatasets: Cora
     ENV["DATADEPS_ALWAYS_ACCEPT"] = true # for MLDatasets
     dataset = Cora()
     classes = dataset.metadata["classes"]

GraphNeuralNetworks/docs/src/tutorials/gnn_intro.md

@@ -92,7 +92,7 @@ karate.node_data.labels_comm
 Now we convert the single-graph dataset to a `GNNGraph`. Moreover, we add a an array of node features, a **34-dimensional feature vector**  for each node which uniquely describes the members of the karate club. We also add a training mask selecting the nodes to be used for training in our semi-supervised node classification task.
 
 ````julia
-g = mldataset2gnngraph(dataset) # convert a MLDataset.jl's dataset to a GNNGraphs (or a collection of graphs)
+g = mldataset2gnngraph(dataset) # convert a MLDatasets.jl's dataset to a GNNGraphs (or a collection of graphs)
 
 x = zeros(Float32, g.num_nodes, g.num_nodes)
 x[diagind(x)] .= 1

GraphNeuralNetworks/docs/src_tutorials/introductory_tutorials/gnn_intro.jl

@@ -42,7 +42,7 @@ karate.node_data.labels_comm
 
 # Now we convert the single-graph dataset to a `GNNGraph`. Moreover, we add a an array of node features, a **34-dimensional feature vector**  for each node which uniquely describes the members of the karate club. We also add a training mask selecting the nodes to be used for training in our semi-supervised node classification task.
 
-g = mldataset2gnngraph(dataset) # convert a MLDataset.jl's dataset to a GNNGraphs (or a collection of graphs)
+g = mldataset2gnngraph(dataset) # convert a MLDatasets.jl's dataset to a GNNGraphs (or a collection of graphs)
 
 x = zeros(Float32, g.num_nodes, g.num_nodes)
 x[diagind(x)] .= 1

GraphNeuralNetworks/examples/graph_classification_temporalbrains.jl

@@ -1,6 +1,6 @@
 # Example of graph classification when graphs are temporal and modeled as `TemporalSnapshotsGNNGraphs'. 
 # In this code, we train a simple temporal graph neural network architecture to classify subjects' gender (female or male) using the temporal graphs extracted from their brain fMRI scan signals.
-# The dataset used is the TemporalBrains dataset from the MLDataset.jl package, and the accuracy achieved with the model reaches 65-70% (it can be improved by fine-tuning the parameters of the model). 
+# The dataset used is the TemporalBrains dataset from the MLDatasets.jl package, and the accuracy achieved with the model reaches 65-70% (it can be improved by fine-tuning the parameters of the model). 
 # Author: Aurora Rossi
 
 # Load packages

GraphNeuralNetworks/notebooks/gnn_intro.ipynb

@@ -176,7 +176,7 @@
     }
    ],
    "source": [
-    "# convert a MLDataset.jl's dataset to a GNNGraphs (or a collection of graphs)\n",
+    "# convert a MLDatasets.jl's dataset to a GNNGraphs (or a collection of graphs)\n",
     "g = mldataset2gnngraph(dataset)\n",
     "\n",
     "x = zeros(Float32, g.num_nodes, g.num_nodes)\n",