fly6516
6e9c2a5f91
- 新增 Apriori算法挖掘关联规则的实现 - 新增 FP-Growth算法挖掘频繁项集的实现 - 添加相应的数据预处理和结果保存代码 - 优化代码结构，提高可读性和可维护性
59 lines
2.5 KiB
Python
59 lines
2.5 KiB
Python
# -*- coding: utf-8 -*-
|
||
from __future__ import print_function
|
||
import pandas as pd
|
||
|
||
|
||
# 自定义连接函数，用于实现L_{k-1}到C_k的连接
|
||
def connect_string(x, ms):
|
||
x = list(map(lambda i: sorted(i.split(ms)), x))
|
||
l = len(x[0])
|
||
r = []
|
||
for i in range(len(x)):
|
||
for j in range(i, len(x)):
|
||
if x[i][:l - 1] == x[j][:l - 1] and x[i][l - 1] != x[j][l - 1]:
|
||
r.append(x[i][:l - 1] + sorted([x[j][l - 1], x[i][l - 1]]))
|
||
return r
|
||
|
||
|
||
# 寻找关联规则的函数
|
||
def find_rule(d, support, confidence, ms=u'--'):
|
||
result = pd.DataFrame(index=['support', 'confidence']) # 定义输出结果
|
||
|
||
support_series = 1.0 * d.sum() / len(d) # 支持度序列
|
||
column = list(support_series[support_series > support].index) # 初步根据支持度筛选
|
||
k = 0
|
||
|
||
while len(column) > 1:
|
||
k = k + 1
|
||
print(u'\n正在进行第%s次搜索...' % k)
|
||
column = connect_string(column, ms)
|
||
print(u'数目：%s...' % len(column))
|
||
sf = lambda i: d[i].prod(axis=1, numeric_only=True) # 新一批支持度的计算函数
|
||
|
||
# 创建连接数据，这一步耗时、耗内存最严重。当数据集较大时，可以考虑并行运算优化。
|
||
d_2 = pd.DataFrame(list(map(sf, column)), index=[ms.join(i) for i in column]).T
|
||
|
||
support_series_2 = 1.0 * d_2[[ms.join(i) for i in column]].sum() / len(d) # 计算连接后的支持度
|
||
column = list(support_series_2[support_series_2 > support].index) # 新一轮支持度筛选
|
||
support_series = pd.concat([support_series, support_series_2])
|
||
column2 = []
|
||
|
||
for i in column: # 遍历可能的推理，如{A,B,C}究竟是A+B-->C还是B+C-->A还是C+A-->B？
|
||
i = i.split(ms)
|
||
for j in range(len(i)):
|
||
column2.append(i[:j] + i[j + 1:] + i[j:j + 1])
|
||
|
||
cofidence_series = pd.Series(index=[ms.join(i) for i in column2]) # 定义置信度序列
|
||
|
||
for i in column2: # 计算置信度序列
|
||
cofidence_series[ms.join(i)] = support_series[ms.join(sorted(i))] / support_series[ms.join(i[:len(i) - 1])]
|
||
|
||
for i in cofidence_series[cofidence_series > confidence].index: # 置信度筛选
|
||
result.loc[i, 'confidence'] = cofidence_series[i] # 使用 .loc 更新置信度
|
||
result.loc[i, 'support'] = support_series[ms.join(sorted(i.split(ms)))] # 使用 .loc 更新支持度
|
||
|
||
result = result.T.sort_values(['confidence', 'support'], ascending=False) # 结果整理，输出
|
||
print(u'\n结果为：')
|
||
print(result)
|
||
|
||
return result |