[Misc] Update comments in python code (#6551)

examples/sampling/graphbolt/link_prediction.py

@@ -83,7 +83,8 @@ def create_dataloader(args, graph, features, itemset, is_train=True):
     """Get a GraphBolt version of a dataloader for link prediction tasks. This
     function demonstrates how to utilize functional forms of datapipes in
     GraphBolt. Alternatively, you can create a datapipe using its class
-    constructor.
+    constructor. For a more detailed tutorial, please read the examples in
+    `dgl/notebooks/graphbolt/walkthrough.ipynb`.
     """
 
     ############################################################################

examples/sampling/graphbolt/node_classification.py

@@ -56,7 +56,8 @@ def create_dataloader(
     [HIGHLIGHT]
     Get a GraphBolt version of a dataloader for node classification tasks.
     This function demonstrates how to utilize functional forms of datapipes in
-    GraphBolt.
+    GraphBolt. For a more detailed tutorial, please read the examples in
+    `dgl/notebooks/graphbolt/walkthrough.ipynb`.
     Alternatively, you can create a datapipe using its class constructor.
 
     Parameters

python/dgl/graphbolt/impl/fused_csc_sampling_graph.py

@@ -143,15 +143,15 @@ class FusedCSCSamplingGraph(SamplingGraph):
         >>> total_num_edges = 12
         >>> ntypes = {"N0": 0, "N1": 1}
         >>> etypes = {"N0:R0:N0": 0, "N0:R1:N1": 1,
-        ... "N1:R2:N0": 2, "N1:R3:N1": 3}
+        ...     "N1:R2:N0": 2, "N1:R3:N1": 3}
         >>> indptr = torch.LongTensor([0, 3, 5, 7, 9, 12])
         >>> indices = torch.LongTensor([0, 1, 4, 2, 3, 0, 1, 1, 2, 0, 3, 4])
         >>> node_type_offset = torch.LongTensor([0, 2, 5])
         >>> type_per_edge = torch.LongTensor(
-        ... [0, 0, 2, 2, 2, 1, 1, 1, 3, 1, 3, 3])
+        ...     [0, 0, 2, 2, 2, 1, 1, 1, 3, 1, 3, 3])
         >>> metadata = gb.GraphMetadata(ntypes, etypes)
         >>> graph = gb.from_fused_csc(indptr, indices, node_type_offset,
-        ... type_per_edge, None, metadata)
+        ...     type_per_edge, None, metadata)
         >>> print(graph.num_nodes)
         {'N0': 2, 'N1': 3}
         """
@@ -772,7 +772,6 @@ def from_fused_csc(
     ...             node_type_offset=node_type_offset,
     ...             type_per_edge=type_per_edge,
     ...             edge_attributes=None, metadata=metadata)
-    None, metadata)
     >>> print(graph)
     FusedCSCSamplingGraph(csc_indptr=tensor([0, 2, 5, 7]),
                      indices=tensor([1, 3, 0, 1, 2, 0, 3]),

python/dgl/graphbolt/impl/neighbor_sampler.py

@@ -63,15 +63,14 @@ class NeighborSampler(SubgraphSampler):
     >>> node_pairs = torch.LongTensor([[0, 1], [1, 2]])
     >>> item_set = gb.ItemSet(node_pairs, names="node_pairs")
     >>> item_sampler = gb.ItemSampler(
-        ...item_set, batch_size=1,
-        ...)
+    ...     item_set, batch_size=1,)
     >>> neg_sampler = gb.UniformNegativeSampler(
-        ...item_sampler, graph, 2)
+    ...     item_sampler, graph, 2)
     >>> subgraph_sampler = gb.NeighborSampler(
-        ...neg_sampler, graph, [5, 10, 15])
+    ...     neg_sampler, graph, [5, 10, 15])
     >>> for data in subgraph_sampler:
-        ... print(data.compacted_node_pairs)
-        ... print(len(data.sampled_subgraphs))
+    ...     print(data.compacted_node_pairs)
+    ...     print(len(data.sampled_subgraphs))
     (tensor([0, 0, 0]), tensor([1, 0, 2]))
     3
     (tensor([0, 0, 0]), tensor([1, 1, 1]))
@@ -197,17 +196,16 @@ class LayerNeighborSampler(NeighborSampler):
     >>> node_pairs = torch.LongTensor([[0, 1], [1, 2]])
     >>> item_set = gb.ItemSet(node_pairs, names="node_pairs")
     >>> item_sampler = gb.ItemSampler(
-        ...item_set, batch_size=1,
-        ...)
+    ...     item_set, batch_size=1,)
     >>> neg_sampler = gb.UniformNegativeSampler(
-        ...item_sampler, 2, data_format, graph)
+    ...     item_sampler, 2, data_format, graph)
     >>> fanouts = [torch.LongTensor([5]), torch.LongTensor([10]),
-        ...torch.LongTensor([15])]
+    ...     torch.LongTensor([15])]
     >>> subgraph_sampler = gb.LayerNeighborSampler(
-        ...neg_sampler, graph, fanouts)
+    ...     neg_sampler, graph, fanouts)
     >>> for data in subgraph_sampler:
-        ... print(data.compacted_node_pairs)
-        ... print(len(data.sampled_subgraphs))
+    ...      print(data.compacted_node_pairs)
+    ...      print(len(data.sampled_subgraphs))
     (tensor([0, 0, 0]), tensor([1, 0, 2]))
     3
     (tensor([0, 0, 0]), tensor([1, 1, 1]))

python/dgl/graphbolt/impl/uniform_negative_sampler.py

@@ -36,14 +36,12 @@ class UniformNegativeSampler(NegativeSampler):
     >>> node_pairs = (torch.tensor([0, 1]), torch.tensor([1, 2]))
     >>> item_set = gb.ItemSet(node_pairs, names="node_pairs")
     >>> item_sampler = gb.ItemSampler(
-        ...item_set, batch_size=1,
-        ...)
+    ...     item_set, batch_size=1,)
     >>> neg_sampler = gb.UniformNegativeSampler(
-        ...item_sampler, graph, 2)
+    ...     item_sampler, graph, 2)
     >>> for minibatch in neg_sampler:
-        ...  print(minibatch.negative_srcs)
-        ...  print(minibatch.negative_dsts)
-        ...
+    ...       print(minibatch.negative_srcs)
+    ...       print(minibatch.negative_dsts)
     (tensor([0, 0, 0]), tensor([1, 1, 2]), tensor([1, 0, 0]))
     (tensor([1, 1, 1]), tensor([2, 1, 2]), tensor([1, 0, 0]))
     """