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------'

python/dgl/data/ppi.py

@@ -59,7 +59,7 @@ class PPIDataset(DGLBuiltinDataset):
     Examples
     --------
     >>> dataset = PPIDataset(mode='valid')
-    >>> num_labels = dataset.num_labels
+    >>> num_classes = dataset.num_classes
     >>> for g in dataset:
     ....    feat = g.ndata['feat']
     ....    label = g.ndata['label']
@@ -173,6 +173,10 @@ class PPIDataset(DGLBuiltinDataset):
     def num_labels(self):
         return 121
 
+    @property
+    def num_classes(self):
+        return 121
+
     def __len__(self):
         """Return number of samples in this dataset."""
         return len(self.graphs)

python/dgl/data/qm7b.py

@@ -141,6 +141,11 @@ class QM7bDataset(DGLDataset):
         """Number of prediction tasks."""
         return 14
 
+    @property
+    def num_classes(self):
+        """Number of prediction tasks."""
+        return 14
+
     def __getitem__(self, idx):
         r"""Get graph and label by index
 

python/dgl/data/qm9.py

@@ -157,6 +157,16 @@ class QM9Dataset(DGLDataset):
         """
         return self.label.shape[1]
 
+    @property
+    def num_classes(self):
+        r"""
+        Returns
+        --------
+        int
+            Number of prediction tasks.
+        """
+        return self.label.shape[1]
+
     @property
     def num_tasks(self):
         r"""