def f1_smart(y_true, y_pred):
    args = np.argsort(y_pred)
    tp = y_true.sum()
    fs = (tp - np.cumsum(y_true[args[:-1]])) / np.arange(y_true.shape[0] + tp - 1, tp, -1)
    res_idx = np.argmax(fs)
    return 2 * fs[res_idx], (y_pred[args[res_idx]] + y_pred[args[res_idx + 1]]) / 2