首先当然要配置r语言环境变量什么的
D:\R-3.5.1\bin\x64;
D:\R-3.5.1\bin\x64\R.dll;
D:\R-3.5.1;
D:\ProgramData\Anaconda3\Lib\site-packages\rpy2;
本来用python也可以实现关联规则，虽然没包，但是可视化挺麻烦的
#!/usr/bin/env python3 # -*- coding: utf-8 -*- from pandas import read_csv def loadDataSet(): dataset = read_csv("F:/goverment/Aprior/No Number.csv") data = dataset.values[:,:] Data=[] for line in data: ls=[] for i in line: ls.append(i) Data.append(ls) #print(Data) return Data ''' return [['a', 'c', 'e'], ['b', 'd'], ['b', 'c'], ['a', 'b', 'c', 'd'], ['a', 'b'], ['b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'e'], ['a', 'b', 'c'], ['a', 'c', 'e']]''' def createC1(dataSet): C1 = [] for transaction in dataSet: for item in transaction: if not [item] in C1: C1.append([item]) C1.sort() '''??????????????????????????????????????????????????????''' # 映射为frozenset唯一性的，可使用其构造字典 return list(map(frozenset, C1)) # 从候选K项集到频繁K项集（支持度计算） def scanD(D, Ck, minSupport): ssCnt = {} for tid in D: for can in Ck: if can.issubset(tid): if not can in ssCnt: ssCnt[can] = 1 else: ssCnt[can] += 1 numItems = float(len(D)) retList = [] supportData = {} for key in ssCnt: support = ssCnt[key] / numItems if support >= minSupport: retList.insert(0, key) supportData[key] = support return retList, supportData def calSupport(D, Ck, min_support): dict_sup = {} for i in D: for j in Ck: if j.issubset(i): if not j in dict_sup: dict_sup[j] = 1 else: dict_sup[j] += 1 sumCount = float(len(D)) supportData = {} relist = [] for i in dict_sup: temp_sup = dict_sup[i] / sumCount if temp_sup >= min_support: relist.append(i) supportData[i] = temp_sup # 此处可设置返回全部的支持度数据（或者频繁项集的支持度数据） return relist, supportData # 改进剪枝算法 def aprioriGen(Lk, k): # 创建候选K项集 ##LK为频繁K项集 retList = [] lenLk = len(Lk) for i in range(lenLk): for j in range(i + 1, lenLk): L1 = list(Lk[i])[:k - 2] L2 = list(Lk[j])[:k - 2] L1.sort() L2.sort() if L1 == L2: # 前k-1项相等，则可相乘，这样可防止重复项出现 # 进行剪枝（a1为k项集中的一个元素，b为它的所有k-1项子集） a = Lk[i] | Lk[j] # a为frozenset()集合 a1 = list(a) b = [] # 遍历取出每一个元素，转换为set，依次从a1中剔除该元素，并加入到b中 for q in range(len(a1)): t = [a1[q]] tt = frozenset(set(a1) - set(t)) b.append(tt) t = 0 for w in b: # 当b（即所有k-1项子集）都是Lk（频繁的）的子集，则保留，否则删除。 if w in Lk: t += 1 if t == len(b): retList.append(b[0] | b[1]) return retList def apriori(dataSet, minSupport=0.2): C1 = createC1(dataSet) D = list(map(set, dataSet)) # 使用list()转换为列表 L1, supportData = calSupport(D, C1, minSupport) L = [L1] # 加列表框，使得1项集为一个单独元素 k = 2 while (len(L[k - 2]) > 0): Ck = aprioriGen(L[k - 2], k) Lk, supK = scanD(D, Ck, minSupport) # scan DB to get Lk supportData.update(supK) L.append(Lk) # L最后一个值为空集 k += 1 del L[-1] # 删除最后一个空集 return L, supportData # L为频繁项集，为一个列表，1，2，3项集分别为一个元素。 # 生成集合的所有子集 def getSubset(fromList, toList): for i in range(len(fromList)): t = [fromList[i]] tt = frozenset(set(fromList) - set(t)) if not tt in toList: toList.append(tt) tt = list(tt) if len(tt) > 1: getSubset(tt, toList) #def calcConf(freqSet, H, supportData, ruleList, minConf=0.7): def calcConf(freqSet, H, supportData, Rule, minConf=0.7): for conseq in H: conf = supportData[freqSet] / supportData[freqSet - conseq] # 计算置信度 # 提升度lift计算lift = p(a & b) / p(a)*p(b) lift = supportData[freqSet] / (supportData[conseq] * supportData[freqSet - conseq]) ls=[] if conf >= minConf and lift > 3: for i in freqSet - conseq: #print(i," ",end="") ls.append(i) ls.append(" ") #print('-->',end="") ls.append('-->') for i in conseq: #print(i," ",end="") ls.append(i) ls.append(" ") #print('支持度:', round(supportData[freqSet - conseq]*100, 1), "%",' 置信度:', round(conf*100,1),"%",' lift值为', round(lift, 2)) #ls.append(' 支持度:') #ls.append(round(supportData[freqSet - conseq]*100, 1)) #ls.append("% ") #ls.append(' 置信度:') ls.append( round(conf*100,1)) ls.append("% ") #ls.append( round(lift, 2)) #ls.append(round(lift, 2)) #ruleList.append((freqSet - conseq, conseq, conf)) if ls!=[]: #print(len(ls)) Rule.append(ls) # ============================================================================= # for line in Rule: # for i in line: # print(i,end="") # print("") # ============================================================================= return Rule # ============================================================================= # print(freqSet - conseq, '-->', conseq, '支持度', round(supportData[freqSet - conseq], 2), '置信度：', round(conf,3), # 'lift值为：', round(lift, 2)) # ============================================================================= # 生成规则 def gen_rule(L, supportData, minConf=0.7): bigRuleList = [] for i in range(1, len(L)): # 从二项集开始计算 for freqSet in L[i]: # freqSet为所有的k项集 # 求该三项集的所有非空子集，1项集，2项集，直到k-1项集，用H1表示，为list类型,里面为frozenset类型， H1 = list(freqSet) all_subset = [] getSubset(H1, all_subset) # 生成所有的子集 calcConf(freqSet, all_subset, supportData, bigRuleList, minConf) return bigRuleList if __name__ == '__main__': dataSet = loadDataSet() #print(dataSet) L, supportData = apriori(dataSet, minSupport=0.05) rule = gen_rule(L, supportData, minConf=0.5) for i in rule: for j in i: if j==',': continue else: print(j,end="") print("") ''' 具体公式： P(B|A)/P(B) 称为A条件对于B事件的提升度,如果该值=1,说明两个条件没有任何关联, 如果<1,说明A条件(或者说A事件的发生)与B事件是相斥的, 一般在数据挖掘中当提升度大于3时,我们才承认挖掘出的关联规则是有价值的。 '''