编写python代码实现apriori算法。

以下是Python实现Apriori算法的示例代码：

def load_data_set():
    data_set = [['apple', 'beer', 'rice', 'chicken'],
                ['apple', 'beer', 'rice'],
                ['apple', 'beer'],
                ['apple', 'banana', 'chicken'],
                ['apple', 'banana']]
    return data_set

def create_c1(data_set):
    c1 = []
    for transaction in data_set:
        for item in transaction:
            if not [item] in c1:
                c1.append([item])
    c1.sort()
    return list(map(frozenset, c1))

def scan_data_set(data_set, candidate_set, min_support):
    support_count = {}
    for transaction in data_set:
        for candidate in candidate_set:
            if candidate.issubset(transaction):
                if not candidate in support_count:
                    support_count[candidate] = 1
                else:
                    support_count[candidate] += 1
    num_items = float(len(data_set))
    frequent_set = []
    support_data = {}
    for key in support_count:
        support = support_count[key] / num_items
        if support >= min_support:
            frequent_set.append(key)
        support_data[key] = support
    return frequent_set, support_data

def apriori_gen(frequent_set, k):
    new_frequent_set = []
    len_frequent_set = len(frequent_set)
    for i in range(len_frequent_set):
        for j in range(i + 1, len_frequent_set):
            l1 = list(frequent_set[i])[:k - 2]
            l2 = list(frequent_set[j])[:k - 2]
            l1.sort()
            l2.sort()
            if l1 == l2:
                new_frequent_set.append(frequent_set[i] | frequent_set[j])
    return new_frequent_set

def apriori(data_set, min_support=0.5):
    candidate_set = create_c1(data_set)
    frequent_set_1, support_data = scan_data_set(data_set, candidate_set, min_support)
    frequent_set = [frequent_set_1]
    k = 2
    while len(frequent_set[k - 2]) > 0:
        candidate_set = apriori_gen(frequent_set[k - 2], k)
        frequent_set_k, support_k = scan_data_set(data_set, candidate_set, min_support)
        support_data.update(support_k)
        frequent_set.append(frequent_set_k)
        k += 1
    return frequent_set, support_data

data_set = load_data_set()
frequent_set, support_data = apriori(data_set, min_support=0.5)
print(frequent_set)
print(support_data)

在上面的代码中，我们定义了 `load_data_set` 函数用于加载数据集， `create_c1` 函数用于创建候选项集 `C1` ， `scan_data_set` 函数用于扫描数据集并获取频繁项集和支持度字典， `apriori_gen` 函数用于生成 `k` 项频繁项集的候选项集， `apriori` 函数是Apriori算法的主体部分。

我们通过调用 `apriori` 函数来运行整个算法，并将频繁项集和支持度输出到控制台。