为了将数据集分为train、validation和test，我尝试使用以下函数：

X = df.drop("RISK DECISION", axis = 1).values
y = df["RISK DECISION"].values

def train_validation_test_split(X, y, validation_size = 0.1, test_size = 0.1, random_state = None):
    
    if random_state != None:
        np.random.seed(random_state)
    
    n = X.shape[0]
    
    validation_indices = np.random.choice(n, int(n*validation_size), replace = False)
    test_indices = np.random.choice(n, int(n*test_size), replace = False)
    all_indices = np.concatenate((validation_indices, test_indices))
    
    X_validation = X[validation_indices]
    y_validation = y[validation_indices]
    X_test = X[test_indices]
    y_test = y[test_indices]
    
    X_train = np.delete(X, all_indices, axis = 0)
    y_train = np.delete(y, all_indices, axis = 0)
    
    return(X_train, X_validation, X_test, y_train, y_validation, y_test)

初始数据集的长度为：34322条记录
应用函数后，X_train、X_validation和X_test的长度之和大于数据集的初始长度。
问题可能由np.random.choice给出，变量validation_indices和test_indices可能包含一些相同的索引。
如何在不过多修改train_validation_test_split函数的情况下解决这个问题？