Accuracy or similar metrics used for `autonet.score` about auto-pytorch HOT 3 CLOSED

def qloss(y_true, y_pred, qs):
        e = y_true - y_pred
        return np.maximum(qs * e, (qs-1)*e).mean()
metric_ql = functools.partial(qloss, qs = np.array([[0.1, 0.5, 0.9]]))

def qinputloss(y_true, y_pred, ws):
    w_q = ws[0,0]
    w_input = ws[0:1, 1:]
    q = y_true[:, 0:1]

    e_q = np.abs(y_true[:,0]-y_pred[:,0])
    e = y_true[:, 1:] - y_pred[:, 1:]

    #print(w_input)
    #print(q*e, (q-1)*e)

    ret = w_input * np.maximum(q*e, (q-1)*e)
    #print(ret)

    return np.mean(ret)+ np.mean(w_q*e_q)
metric_qil = functools.partial(qinputloss, ws=np.array([[0,1]]))

metric_selector = autonet.pipeline[MetricSelector.get_name()]
metric_selector.add_metric('metric_ql', metric_ql, loss_transform=False, requires_target_class_labels=False)
metric_selector.add_metric('metric_qil', metric_qil, loss_transform=False, requires_target_class_labels=False)

results_fit = autonet.fit(X_train=train_x_wq,
                          Y_train=train_y_wq,
                          
                          loss_modules=["qinput_loss"], 
                          optimize_metric = 'metric_qil')
						  

def QLossMetric(qs):
    if len(qs.shape) < 1:
        raise ValueError('qs should be 1-dim or 2-dim array')
    elif len(qs.shape) == 1:
        qs = qs.reshape(1,-1)
    elif len(qs.shape) > 2:
        raise ValueError('qs should be 1-dim or 2-dim array')
    def qloss(y_true, y_pred):
        e = y_true - y_pred
        return np.maximum(qs * e, (qs-1)*e).mean()
    return qloss