from itertools import combinations

 data = [['I1', 'I2', 'I5'], ['I2', 'I4'], ['I2', 'I3'], ['I1', 'I2', 'I4'], ['I1', 'I3'],

         ['I2', 'I3'], ['I1', 'I3'], ['I1', 'I2', 'I3', 'I5'], ['I1', 'I2', 'I3']]

 # 候选集生成

 # 输入：

 # f_set: k-1项集, k:项集个数

 # 输出：

 # k_cand：k项候选集

 def apriori_gen(f_set, k):

     k_cand = []

     temp = [frozenset(l) for l in combinations(f_set, k)]

     for t in temp:

         if has_infrequent_subset(t, f_set):

             del t

         else:

             k_cand.append(t)

     return k_cand

 # 非频繁项集的超集也是非频繁的

 def has_infrequent_subset(c_set, f_set):

     for subset in c_set:

         if not frozenset([subset]).issubset(f_set):

             return True

     return False

 # 输入（绝对）最小支持度, min_sup

 # 输出：全部频繁项集（不包括一项集）, all_f_set

 def get_f_set(min_sup=2):

     all_f_set = []

     L1 = frozenset([d for ds in data for d in ds])

     k = 2

     size = len(L1)

     while k <= size:

         c_k = frozenset(apriori_gen(L1, k))

         for c in c_k:

             count = 0

             for d in data:

                 if c.issubset(frozenset(d)):

                     count += 1

             if count >= min_sup:

                 all_f_set.append((c, count))

         k += 1

     return all_f_set

 if __name__ == '__main__':

     all_frequent_set = get_f_set()

     for i in all_frequent_set:

         print(i)