Fix num_labels to num_classes in dataset files (#6666)

examples/mxnet/appnp/appnp.py

@@ -93,7 +93,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = data.graph.number_of_edges()
     print(
         """----Data statistics------'

examples/mxnet/gat/train.py

@@ -69,7 +69,7 @@ def main(args):
     test_mask = g.ndata["test_mask"]
     test_mask = mx.nd.array(np.nonzero(test_mask.asnumpy())[0], ctx=ctx)
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = data.graph.number_of_edges()
 
     g = dgl.remove_self_loop(g)

examples/mxnet/gcn/gcn_concat.py

@@ -94,7 +94,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = data.graph.number_of_edges()
     print(
         """----Data statistics------'

examples/mxnet/gcn/train.py

@@ -46,7 +46,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = data.graph.number_of_edges()
     print(
         """----Data statistics------'

examples/mxnet/graphsage/main.py

@@ -111,7 +111,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = data.graph.number_of_edges()
     print(
         """----Data statistics------'

examples/mxnet/monet/citation.py

@@ -93,7 +93,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = data.graph.number_of_edges()
     print(
         """----Data statistics------'

examples/mxnet/sgc/sgc.py

@@ -56,7 +56,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = data.graph.number_of_edges()
     print(
         """----Data statistics------'

examples/mxnet/tagcn/train.py

@@ -48,7 +48,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = data.graph.number_of_edges()
     print(
         """----Data statistics------'

examples/pytorch/dimenet/qm9.py

@@ -86,7 +86,7 @@ class QM9(QM9Dataset):
     Examples
     --------
     >>> data = QM9Dataset(label_keys=['mu', 'gap'], cutoff=5.0)
-    >>> data.num_labels
+    >>> data.num_classes
     2
     >>>
     >>> # iterate over the dataset

examples/pytorch/gat/train_ppi.py

@@ -116,7 +116,7 @@ if __name__ == "__main__":
 
     # create GAT model
     in_size = features.shape[1]
-    out_size = train_dataset.num_labels
+    out_size = train_dataset.num_classes
     model = GAT(in_size, 256, out_size, heads=[4, 4, 6]).to(device)
 
     # model training

examples/pytorch/geniepath/ppi.py

@@ -49,7 +49,7 @@ def main(args):
     else:
         device = "cpu"
 
-    num_classes = train_dataset.num_labels
+    num_classes = train_dataset.num_classes
 
     # Extract node features
     graph = train_dataset[0]

examples/pytorch/sgc/sgc.py

@@ -59,7 +59,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = g.num_edges()
     print(
         """----Data statistics------'

examples/pytorch/sgc/sgc_reddit.py

@@ -51,7 +51,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = g.num_edges()
     print(
         """----Data statistics------'

examples/pytorch/sign/dataset.py

@@ -28,7 +28,7 @@ def load_dataset(name):
 
             data = CitationGraphDataset("cora")
             g = data[0]
-        n_classes = data.num_labels
+        n_classes = data.num_classes
         train_mask = g.ndata["train_mask"]
         val_mask = g.ndata["val_mask"]
         test_mask = g.ndata["test_mask"]

examples/pytorch/tagcn/train.py

@@ -38,7 +38,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = g.num_edges()
     print(
         """----Data statistics------'

examples/tensorflow/dgi/train.py

@@ -50,7 +50,7 @@ def main(args):
         val_mask = g.ndata["val_mask"]
         test_mask = g.ndata["test_mask"]
         in_feats = features.shape[1]
-        n_classes = data.num_labels
+        n_classes = data.num_classes
         n_edges = g.number_of_edges()
 
         # add self loop

examples/tensorflow/gat/train.py

@@ -66,7 +66,7 @@ def main(args):
         val_mask = g.ndata["val_mask"]
         test_mask = g.ndata["test_mask"]
         num_feats = features.shape[1]
-        n_classes = data.num_labels
+        n_classes = data.num_classes
         n_edges = g.number_of_edges()
         print(
             """----Data statistics------'

examples/tensorflow/gcn/gcn_builtin.py

@@ -114,7 +114,7 @@ def main(args):
         val_mask = g.ndata["val_mask"]
         test_mask = g.ndata["test_mask"]
         in_feats = features.shape[1]
-        n_classes = data.num_labels
+        n_classes = data.num_classes
         n_edges = data.graph.number_of_edges()
         print(
             """----Data statistics------'

examples/tensorflow/gcn/gcn_mp.py

@@ -121,7 +121,7 @@ def main(args):
         val_mask = g.ndata["val_mask"]
         test_mask = g.ndata["test_mask"]
         in_feats = features.shape[1]
-        n_classes = data.num_labels
+        n_classes = data.num_classes
         n_edges = data.graph.number_of_edges()
         print(
             """----Data statistics------'

examples/tensorflow/gcn/train.py

@@ -43,7 +43,7 @@ def main(args):
         val_mask = g.ndata["val_mask"]
         test_mask = g.ndata["test_mask"]
         in_feats = features.shape[1]
-        n_classes = data.num_labels
+        n_classes = data.num_classes
         n_edges = g.number_of_edges()
         print(
             """----Data statistics------'