[GraphBolt] fix several issues in doc and notebook (#6775)

docs/source/guide/minibatch-custom-sampler.rst

@@ -5,12 +5,12 @@
 
 Implementing custom samplers involves subclassing the
 :class:`dgl.graphbolt.SubgraphSampler` base class and implementing its abstract
-:attr:`_sample_subgraphs` method. The :attr:`_sample_subgraphs` method should
+:attr:`sample_subgraphs` method. The :attr:`sample_subgraphs` method should
 take in seed nodes which are the nodes to sample neighbors from:
 
 .. code:: python
 
-    def _sample_subgraphs(self, seed_nodes):
+    def sample_subgraphs(self, seed_nodes):
         return input_nodes, sampled_subgraphs
 
 The method should return the input node IDs list and a list of subgraphs. Each
@@ -31,7 +31,7 @@ The code below implements a classical neighbor sampler:
            self.graph = graph
            self.fanouts = fanouts
 
-       def _sample_subgraphs(self, seed_nodes):
+       def sample_subgraphs(self, seed_nodes):
            subgs = []
            for fanout in reversed(self.fanouts):
                # Sample a fixed number of neighbors of the current seed nodes.

docs/source/guide/minibatch-sparse.rst

@@ -29,7 +29,7 @@ will customize another sampler with DGL sparse library as shown below.
                     fanout = torch.LongTensor([int(fanout)])
                 self.fanouts.insert(0, fanout)
 
-        def _sample_subgraphs(self, seeds):
+        def sample_subgraphs(self, seeds):
             sampled_matrices = []
             src = seeds
 

notebooks/stochastic_training/link_prediction.ipynb

@@ -54,7 +54,8 @@
         "os.environ['TORCH'] = torch.__version__\n",
         "os.environ['DGLBACKEND'] = \"pytorch\"\n",
         "\n",
-        "# Install the CPU version.\n",
+        "# Install the CPU version. If you want to install CUDA version, please\n",
+        "# refer to https://www.dgl.ai/pages/start.html.\n",
         "device = torch.device(\"cpu\")\n",
         "!pip install --pre dgl -f https://data.dgl.ai/wheels-test/repo.html\n",
         "\n",
@@ -339,7 +340,7 @@
         "# Compute the AUROC score.\n",
         "from sklearn.metrics import roc_auc_score\n",
         "\n",
-        "auc = roc_auc_score(labels, logits)\n",
+        "auc = roc_auc_score(labels.cpu(), logits.cpu())\n",
         "print(\"Link Prediction AUC:\", auc)"
       ],
       "metadata": {

notebooks/stochastic_training/node_classification.ipynb

@@ -54,7 +54,8 @@
         "os.environ['TORCH'] = torch.__version__\n",
         "os.environ['DGLBACKEND'] = \"pytorch\"\n",
         "\n",
-        "# Install the CPU version.\n",
+        "# Install the CPU version. If you want to install CUDA version, please\n",
+        "# refer to https://www.dgl.ai/pages/start.html.\n",
         "device = torch.device(\"cpu\")\n",
         "!pip install --pre dgl -f https://data.dgl.ai/wheels-test/repo.html\n",
         "\n",