format

python/graphscope/learning/graph.py

@@ -56,14 +56,19 @@ def __init__(self, session, graph, nodes=None, edges=None, gen_labels=None):
             session (:class:`Session`): instance of GraphScope session.
             graph (:class:`graphscope.framework.graph.GraphDAGNode`):
                 Source property graph.
-            nodes (list, optional): list of node types that will be used for gnn training, the element of list
-                can be `node_label` or `(node_label, features)`. If the element of list
-                is a tuple and contains selected feature list, it would use the selected feature list for training.
-                if nodes is None, use all type of nodes and features for the gnn training.
-            edges (list, optional): list of edge types that will be used for gnn training. we use `(src_label, edge_label, dst_label)`
-                to specify one edge type. if edges is None, use all type of edges for gnn training.
-            gen_labels (list, optional): Alias node and edge labels and extract train/validation/test dataset
-                from original graph for supervised gnn training. We will explains the details in the examples.
+            nodes (list, optional): list of node types that will be used for gnn
+                training, the element of list can be `node_label` or
+                `(node_label, features)`. If the element of list is a tuple and
+                contains selected feature list, it would use the selected
+                feature list for training. if nodes is None, use all type of
+                nodes and features for the gnn training.
+            edges (list, optional): list of edge types that will be used for gnn
+                training. we use `(src_label, edge_label, dst_label)`
+                to specify one edge type. if edges is None, use all type of
+                edges for gnn training.
+            gen_labels (list, optional): Alias node and edge labels and extract
+                train/validation/test dataset from original graph for supervised
+                gnn training. We will explains the details in the examples.
 
         Examples
         --------
@@ -74,9 +79,12 @@ def __init__(self, session, graph, nodes=None, edges=None, gen_labels=None):
                 nodes=[("paper", features)])  # use "paper" node and features for training
                 edges=[("paper", "links", "paper")]  # use the `paper->links->papers` edge type for training
                 gen_labels=[
-                    ("train", "paper", 100, (0, 75)), # cuts "paper" nodes into 100 pieces, and uses random 75 pieces(75%) as traning dataset.
-                    ("val", "paper", 100, (75, 85)), # cuts "paper" nodes into 100 pieces, and uses random 10 pieces(10%) as validation dataset.
-                    ("test", "paper", 100, (85, 100)), # cuts "paper" nodes into 100 pieces, and uses random 15 pieces(15%) as test dataset.
+                    # cuts "paper" nodes into 100 pieces, and uses random 75 pieces(75%) as traning dataset.
+                    ("train", "paper", 100, (0, 75)),
+                    # cuts "paper" nodes into 100 pieces, and uses random 10 pieces(10%) as validation dataset.
+                    ("val", "paper", 100, (75, 85)),
+                    # cuts "paper" nodes into 100 pieces, and uses random 15 pieces(15%) as test dataset.
+                    ("test", "paper", 100, (85, 100)),
                 ]
             )
         Notes that the train, validation and test dataset are not overlapping. And for unsupervised learning
@@ -85,8 +93,9 @@ def __init__(self, session, graph, nodes=None, edges=None, gen_labels=None):
                 nodes=[("paper", features)])  # use "paper" node and features for training
                 edges=[("paper", "links", "paper")]  # use the `paper->links->papers` edge type for training
                 gen_labels=[
-                    ("train", "paper", 100, (0, 100)), # cuts "paper" nodes into 100 pieces, and uses all pieces as traning dataset and just alias "paper" to "train".
-                ]
+                    # cuts "paper" nodes into 100 pieces, and uses all pieces as traning dataset
+                    #   and just alias "paper" to "train".
+                    ("train", "paper", 100, (0, 100)),
         """
         self._session = session
         self._graph = graph