中医认证关联规则挖掘Python分析代码

#-*- 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 = support_series.append(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[i] = 0.0

result[i]['confidence'] = cofidence_series[i]

result[i]['support'] = support_series[ms.join(sorted(i.split(ms)))]

result = result.T.sort_values(['confidence','support'], ascending = False) #结果整理，输出
#pandas-version-0.20.1 好像没有sort函数了，只有sort_values函数

print(u'\n结果为：')

print(result)

return result

#-*- coding: utf-8 -*-

from __future__ import print_function

import pandas as pd

from apriori import * #导入自行编写的apriori函数

import time #导入时间库用来计算用时

inputfile = '../data/apriori.txt' #输入事务集文件

data = pd.read_csv(inputfile, header=None, dtype = object)

start = time.clock() #计时开始

print(u'\n转换原始数据至0-1矩阵...')

ct = lambda x : pd.Series(1, index = x[pd.notnull(x)]) #转换0-1矩阵的过渡函数

b = map(ct, data.as_matrix()) #用map方式执行

data = pd.DataFrame(list(b)).fillna(0) #实现矩阵转换，空值用0填充
#原始代码data = pd.DataFrame(b).fillna(0)报错

end = time.clock() #计时结束

print(u'\n转换完毕，用时：%0.2f秒' %(end-start))

del b #删除中间变量b，节省内存

support = 0.06 #最小支持度

confidence = 0.75 #最小置信度

ms = '---' #连接符，默认'--'，用来区分不同元素，如A--B。需要保证原始表格中不含有该字符

start = time.clock() #计时开始

print(u'\n开始搜索关联规则...')

find_rule(data, support, confidence, ms)

end = time.clock() #计时结束

print(u'\n搜索完成，用时：%0.2f秒' %(end-start))