Good morning.

I’m following this tutotorial: 6.3 Training GNN for Link Prediction with Neighborhood Sampling — DGL 0.7.2 documentation

I have train and test splits, but not sure how to use during the training loop.

I guess I should use the training graph like this:

```
dataloader = dgl.dataloading.EdgeDataLoader(
train_g, train_eid_dict, sampler,
negative_sampler=dgl.dataloading.negative_sampler.Uniform(5),
batch_size=1024,
shuffle=True,
drop_last=False,
num_workers=4)
```

However, I’m not sure of how to use the test one. I would like to use eval AUC to evaluate the model in this graph. Should I create another dataloader for this test graph in order to generate pos and neg evaluations scores during the loop?

Also, since my graphs are heterographs, I would need to adapt this AUC function:

```
def compute_auc(pos_score, neg_score):
scores = torch.cat([pos_score, neg_score]).numpy()
labels = torch.cat(
[torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])]).numpy()
return roc_auc_score(labels, scores)
```

Could I concatenate a the pos_score and neg_score for the different etypes in order to have just one tensor of scores, and then create another tensor with 1s and 0s? Or should I evaluate every etype separately?

Thank you so much.