[GraphBolt] Remove opt `output_cscformat` from examples. (#6834)

examples/sampling/graphbolt/link_prediction.py

@@ -175,11 +175,7 @@ def create_dataloader(args, graph, features, itemset, is_train=True):
     # [Role]:
     # Initialize a neighbor sampler for sampling the neighborhoods of nodes.
     ############################################################################
-    datapipe = datapipe.sample_neighbor(
-        graph,
-        args.fanout,
-        output_cscformat=(args.output_cscformat == "True"),
-    )
+    datapipe = datapipe.sample_neighbor(graph, args.fanout)
 
     ############################################################################
     # [Input]:
@@ -375,12 +371,6 @@ def parse_args():
         choices=["cpu", "cuda"],
         help="Train device: 'cpu' for CPU, 'cuda' for GPU.",
     )
-    parser.add_argument(
-        "--output_cscformat",
-        default="False",
-        choices=["False", "True"],
-        help="Output type of SampledSubgraph. True for csc_formats, False for node_pairs.",
-    )
     return parser.parse_args()
 
 

examples/sampling/graphbolt/node_classification.py

@@ -50,15 +50,7 @@ from tqdm import tqdm
 
 
 def create_dataloader(
-    graph,
-    features,
-    itemset,
-    batch_size,
-    fanout,
-    device,
-    num_workers,
-    job,
-    output_cscformat,
+    graph, features, itemset, batch_size, fanout, device, num_workers, job
 ):
     """
     [HIGHLIGHT]
@@ -113,9 +105,7 @@ def create_dataloader(
     # Initialize a neighbor sampler for sampling the neighborhoods of nodes.
     ############################################################################
     datapipe = datapipe.sample_neighbor(
-        graph,
-        fanout if job != "infer" else [-1],
-        output_cscformat=(output_cscformat == "True"),
+        graph, fanout if job != "infer" else [-1]
     )
 
     ############################################################################
@@ -240,7 +230,6 @@ def layerwise_infer(
         device=args.device,
         num_workers=args.num_workers,
         job="infer",
-        output_cscformat=args.output_cscformat,
     )
     pred = model.inference(graph, features, dataloader, args.device)
     pred = pred[test_set._items[0]]
@@ -268,7 +257,6 @@ def evaluate(args, model, graph, features, itemset, num_classes):
         device=args.device,
         num_workers=args.num_workers,
         job="evaluate",
-        output_cscformat=args.output_cscformat,
     )
 
     for step, data in tqdm(enumerate(dataloader)):
@@ -295,7 +283,6 @@ def train(args, graph, features, train_set, valid_set, num_classes, model):
         device=args.device,
         num_workers=args.num_workers,
         job="train",
-        output_cscformat=args.output_cscformat,
     )
 
     for epoch in range(args.epochs):
@@ -367,12 +354,6 @@ def parse_args():
         choices=["cpu", "cuda"],
         help="Train device: 'cpu' for CPU, 'cuda' for GPU.",
     )
-    parser.add_argument(
-        "--output_cscformat",
-        default="False",
-        choices=["False", "True"],
-        help="Output type of SampledSubgraph. True for csc_formats, False for node_pairs.",
-    )
     return parser.parse_args()