数据挖掘模型——Aprior算法关联规则——python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(['confidence','support'], ascending = False) #结果整理，输出
    
   print(u'\n结果为：')
    
   print(result)
    
   
    
   return result