Note on QM9 pre-processed version + AUROC metric in bipartite GraphSA…

…GE example (#6553)

examples/hetero/bipartite_sage_unsup.py

@@ -5,12 +5,7 @@
 import torch
 import torch.nn.functional as F
 import tqdm
-from sklearn.metrics import (
-    accuracy_score,
-    f1_score,
-    precision_score,
-    recall_score,
-)
+from sklearn.metrics import roc_auc_score
 from torch.nn import Embedding, Linear
 
 import torch_geometric.transforms as T
@@ -236,22 +231,13 @@ def test(loader):
     pred = torch.cat(preds, dim=0).numpy()
     target = torch.cat(targets, dim=0).numpy()
 
-    pred = pred > 0.5
-    acc = accuracy_score(target, pred)
-    prec = precision_score(target, pred)
-    rec = recall_score(target, pred)
-    f1 = f1_score(target, pred)
-
-    return acc, prec, rec, f1
+    return roc_auc_score(target, pred)
 
 
 for epoch in range(1, 21):
     loss = train()
-    val_acc, val_prec, val_rec, val_f1 = test(val_loader)
-    test_acc, test_prec, test_rec, test_f1 = test(test_loader)
-
-    print(f'Epoch: {epoch:03d}, Loss: {loss:4f}')
-    print(f'Val Acc: {val_acc:.4f}, Val Precision {val_prec:.4f}, '
-          f'Val Recall {val_rec:.4f}, Val F1 {val_f1:.4f}')
-    print(f'Test Acc: {test_acc:.4f}, Test Precision {test_prec:.4f}, '
-          f'Test Recall {test_rec:.4f}, Test F1 {test_f1:.4f}')
+    val_auc = test(val_loader)
+    test_auc = test(test_loader)
+
+    print(f'Epoch: {epoch:02d}, Loss: {loss:4f}, Val: {val_auc:.4f}, '
+          f'Test: {test_auc:.4f}')

torch_geometric/datasets/qm9.py

@@ -96,6 +96,12 @@ class QM9(InMemoryDataset):
     | 18     | :math:`C`                        | Rotational constant                                                               | :math:`\textrm{GHz}`                        |
     +--------+----------------------------------+-----------------------------------------------------------------------------------+---------------------------------------------+
 
+    .. note::
+
+        We also provide a pre-processed version of the dataset in case
+        :class:`rdkit` is not installed. The pre-processed version matches with
+        the manually processed version as outlined in :meth:`process`.
+
     Args:
         root (str): Root directory where the dataset should be saved.
         transform (callable, optional): A function/transform that takes in an