49675c507e5afa9165e378fd738a15a16f323078,examples/geniepath.py,,test,#Any#,130

Before Change

            out = model(data.x.to(device), data.edge_index.to(device))
        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, 201):
    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