0880e905186997ec6545a65c277a4848e0505eed,examples/ppi.py,,test,#Any#,59

Before Change

        pred = (out > 0).float().cpu()
        micro_f1 = metrics.f1_score(data.y, pred, average="micro")
        total_micro_f1 += micro_f1 * data.num_graphs
    return total_micro_f1 / len(loader.dataset)


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

After Change

        ys.append(data.y)
        with torch.no_grad():
            out = model(data.x.to(device), data.edge_index.to(device))
        preds.append((out > 0).float().cpu())

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

Instances