帮我加一段代码使其能够计算置信度ef apriori(data, min_sup=0.05): l1, sup_data = create_l1(data, min_sup) l = [l1] k = 2 while len(l[k - 2]) > 0: ck = lk_to_ckk(l[k - 2], k) #将频繁k-1项集合并到候选k项集 lk, supk = ck_to_lk(data, ck, min_sup) #计算每个候选k项集在数据集中出现的频率 sup_data.update(supk) #supk表示每个频繁k项集在数据集中的支持度 l.append(lk) #lk是频繁k项集 k += 1 for key,value in sup_data.items(): printData("{}:{}".format(key,value)) print(sup_data) print(len(sup_data)) return l, sup_data
时间: 2023-12-24 22:05:43 浏览: 28
以下是增加置信度计算的代码:
```python
import pandas as pd
# 计算置信度
def calc_conf(sup_data, rules, min_conf):
pruned_rules = []
for rule in rules:
conf = sup_data[rule[0] | rule[1]] / sup_data[rule[0]]
if conf >= min_conf:
pruned_rules.append((rule[0], rule[1], conf))
return pruned_rules
# Apriori算法
def apriori(data, min_sup=0.05, min_conf=0.5):
l1, sup_data = create_l1(data, min_sup)
l = [l1]
k = 2
while len(l[k - 2]) > 0:
ck = lk_to_ckk(l[k - 2], k)
lk, supk = ck_to_lk(data, ck, min_sup)
sup_data.update(supk)
l.append(lk)
k += 1
# 打印频繁项集及其支持度
for key, value in sup_data.items():
printData("{}:{}".format(key, value))
# 计算关联规则
rules = []
for i in range(1, len(l)):
for freq_set in l[i]:
for subset in map(frozenset, [x for x in combinations(freq_set, r=i)]):
rules.append((subset, freq_set - subset))
# 打印关联规则及其置信度
pruned_rules = calc_conf(sup_data, rules, min_conf)
for rule in pruned_rules:
printData("{}=>{}, conf:{}".format(rule[0], rule[1], rule[2]))
return l, sup_data
```
在上述代码中,我们定义了一个名为`calc_conf`的函数来计算关联规则的置信度,该函数接收三个参数:频繁项集的支持度信息、关联规则列表和最小置信度,返回一个经过剪枝的关联规则列表。我们还修改了`apriori`函数的代码,以在计算频繁项集和支持度的同时计算关联规则及其置信度,并输出结果。