Problems regarding loss function and auroc

[WHan-alter]

Dear Authors,

Thank you for sharing the work and code! I have some problems regarding the AUC-ROC and loss function part. In

dMaSIF/data_iteration.py

Lines 202 to 215 in 0dcc26c

	pos_indices = torch.randperm(len(pos_labels))[:n_points_sample]
	neg_indices = torch.randperm(len(neg_labels))[:n_points_sample]
	pos_preds = pos_preds[pos_indices]
	pos_labels = pos_labels[pos_indices]
	neg_preds = neg_preds[neg_indices]
	neg_labels = neg_labels[neg_indices]
	preds_concat = torch.cat([pos_preds, neg_preds])
	labels_concat = torch.cat([pos_labels, neg_labels])
	loss = F.binary_cross_entropy_with_logits(preds_concat, labels_concat)
	return loss, preds_concat, labels_concat

You seemed to sample a random number of positive and negative samples to compute the loss and AUC-ROC. For loss, this could be fine, but regarding the AUC-ROC, did you use the same pipeline for the test dataset when measuring the model performance? If so, what's the performance given the entire surface?

Best,
Wenkai