69dd0827d09eae0b4e6b95df854b2814175c9248,examples/ppi.py,,test,#Any#,59

Before Change

    y, pred = torch.cat(ys, dim=0), torch.cat(preds, dim=0)
    if pred.sum().item() == 0:
        return 0
    return metrics.f1_score(y.numpy(), pred.numpy(), average="micro")


for epoch in range(1, 101):
    loss = train()

After Change

        preds.append((out > 0).float().cpu())

    y, pred = torch.cat(ys, dim=0).numpy(), torch.cat(preds, dim=0).numpy()
    return f1_score(y, pred, average="micro") if pred.sum() > 0 else 0


for epoch in range(1, 101):
    loss = train()

Instances