import numpy as np
def load_dataSet():
return [[1,3,4],[2,3,5],[1,2,3,5],[2,5]]
def create_can_set1(dataSet):
"""得到单元素项集, 项集后面会作为Key值，所以用forzenset"""
can_set = set()
can_lst = []
for lst in dataSet:
for can in lst:
if not can in can_set:
can_set.add(can)
can_lst.append([can])
return list(map(frozenset, can_lst)) #将canlst中的每一个list都转化成forzenset
def scan_data(dataSet, can_set, min_support):
"""得到达到最小支持度的频繁项集和对应的支持度"""
can_cnt = {}
for lst in dataSet:
for can in can_set:
if can.issubset(lst): #这个项集出现在集合中
can_cnt[can] = can_cnt.get(can, 0) + 1
total = len(dataSet)
ret_lst = [] #达到最小支持度的频繁项集
support_data = {} #达到最小支持度的频繁项集的支持度
for key, value in can_cnt.items():
support = value / total
if support >= min_support:
ret_lst.append(key)
support_data[key] = support
return ret_lst, support_data
def apriori_gen(lk, k):
"""lk为频繁项集列表，k为每个项集元素个数,函数用于生成下一轮k+1个元素的频繁项集"""
ret_lst = [] #下一轮结果
len_lk = len(lk)
for i in range(len_lk):
l1 = list(lk[i])[:k-1]
l1.sort()
for j in range(i+1, len_lk):
l2 = list(lk[j])[:k-1]
l2.sort()
if l1 == l2:
ret_lst.append(lk[i] | lk[j]) #只需要比较前k-2个元素是否相同，相同则组合即可
return ret_lst
def apriori(dataSet, min_support = 0.5):
"""apriori算法挖掘频繁项集函数，生成并记录每一轮的频繁项集和他们的支持度
"""
can_set = create_can_set1(dataSet)
data_set = list(map(set, dataSet)) #将数据集里的列表转换成set，python2不用加list
can_set1, support_data = scan_data(data_set, can_set, min_support) #第一轮频繁项集
can_sets = [can_set1] #L用于记录所有生成的频繁项集 [[第一轮],[第二轮],...]
k = 2
while len(can_sets[k-2]) > 0: #第k-1轮频繁项集不为空
can_setk = apriori_gen(can_sets[k-2], k-1) #用k-1轮，存放在k-2位置上，生成k轮
can_setk, support_k = scan_data(data_set, can_setk, min_support) #过滤支持度过低的
support_data.update(support_k) #将第k轮的结果放入到support_data中
can_sets.append(can_setk)
k += 1
return can_sets, support_data
def generate_rules(L, support_data, min_conf = 0.5):
"""在频繁项集的基础上构建规则"""
big_rule_list = []
sub_set_list = []
for i in range(0, len(L)):
for freq_set in L[i]:
for sub_set in sub_set_list:
if sub_set.issubset(freq_set):
conf = support_data[freq_set] / support_data[freq_set - sub_set]
big_rule = (freq_set - sub_set, sub_set, conf)
if conf >= min_conf and big_rule not in big_rule_list:
# print freq_set-sub_set, " => ", sub_set, "conf: ", conf
big_rule_list.append(big_rule)
sub_set_list.append(freq_set)
return big_rule_list
