def train_and_evl(data, label, args):
    size = int((label.shape[0] * 0.2))
    auc_ = []
    for i in range(5):
        idx = (size * i)
        X_e = data[idx:(idx + size)]
        y_e = label[idx:(idx + size)]
        X_t = np.vstack((data[:idx], data[(idx + size):]))
        y_t = np.hstack((label[:idx], label[(idx + size):]))
        model = LogisticRegression(max_iter=7000).fit(X_t, y_t)
        y_pred = model.predict_proba(X_e)[:, 1]
        (fpr, tpr, thresholds) = roc_curve(y_true=y_e, y_score=y_pred, pos_label=1)
        auc_.append(auc(fpr, tpr))
    return np.mean(auc_)
def train_and_evl(data, label, args):
    size = int((label.shape[0] * 0.2))
    auc_ = []
    for i in range(5):
        idx = (size * i)
        X_e = data[idx:(idx + size)]
        y_e = label[idx:(idx + size)]
        X_t = np.vstack((data[:idx], data[(idx + size):]))
        y_t = np.hstack((label[:idx], label[(idx + size):]))
        model = LogisticRegression(max_iter=7000).fit(X_t, y_t)
        y_pred = model.predict_proba(X_e)[:, 1]
        (fpr, tpr, thresholds) = roc_curve(y_true=y_e, y_score=y_pred, pos_label=1)
        auc_.append(auc(fpr, tpr))
    return np.mean(auc_)