利用sklearn中的方法实现Apriori算法全部代码

对不起，我之前的回答有误，sklearn确实没有现成的Apriori算法实现，需要使用其他库或自己实现。以下是一个简单的Apriori算法实现示例代码，仅供参考：

def load_dataset():
    """
    加载数据集
    """
    return [['milk', 'bread', 'eggs'],
            ['milk', 'bread'],
            ['milk', 'diaper', 'beer', 'eggs'],
            ['bread', 'diaper', 'eggs'],
            ['bread', 'diaper', 'beer']]

def create_c1(dataset):
    """
    创建初始项集C1
    """
    c1 = set()
    for transaction in dataset:
        for item in transaction:
            c1.add(frozenset([item]))
    return c1

def scan_d(dataset, ck, min_support):
    """
    扫描数据集，筛选出满足最小支持度的项集
    """
    item_count = {}
    for transaction in dataset:
        for item in ck:
            if item.issubset(transaction):
                if item not in item_count:
                    item_count[item] = 1
                else:
                    item_count[item] += 1
    num_items = float(len(dataset))
    ret_list = []
    support_data = {}
    for key in item_count:
        support = item_count[key] / num_items
        if support >= min_support:
            ret_list.append(key)
            support_data[key] = support
    return ret_list, support_data

def apriori_gen(lk, k):
    """
    根据频繁(k-1)项集生成候选项集Ck
    """
    ret_list = []
    len_lk = len(lk)
    for i in range(len_lk):
        for j in range(i+1, len_lk):
            l1 = list(lk[i])[:k-2]
            l2 = list(lk[j])[:k-2]
            l1.sort()
            l2.sort()
            if l1 == l2:
                ret_list.append(lk[i] | lk[j])
    return ret_list

def apriori(dataset, min_support=0.5):
    """
    Apriori算法
    """
    c1 = create_c1(dataset)
    dataset = list(map(set, dataset))
    l1, support_data = scan_d(dataset, c1, min_support)
    l = [l1]
    k = 2
    while len(l[k-2]) > 0:
        ck = apriori_gen(l[k-2], k)
        lk, supk = scan_d(dataset, ck, min_support)
        support_data.update(supk)
        l.append(lk)
        k += 1
    return l, support_data

# 测试代码
dataset = load_dataset()
L, support_data = apriori(dataset, min_support=0.6)
print('频繁项集：', L)
print('支持度信息：', support_data)

以上代码实现了Apriori算法的基本流程，包括创建初始项集、扫描数据集筛选频繁项集、根据频繁(k-1)项集生成候选项集Ck、迭代寻找频繁项集等步骤。