Apriori算法进行关联分析
最后附上代码:
#加载数据集 def loadDataSet(): return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]] #构建候选集C1 def createC1(dataSet): C1 = [] for transaction in dataSet: for item in transaction: if not [item] in C1: C1.append([item]) C1.sort() return C1 #use frozen set so we #can use it as a key in a dict dataSet=loadDataSet() data=createC1(dataSet) print(data) #这个函数的作用是返回符合指定支持度的集合,它有三个参数,参数分别是数据集,候选项集列表CK,以及感兴趣项集的最小支持度minSupport #这个函数用于从Ck生成Lk (k是指数字) def scanD(D, Ck, minSupport): ssCnt = {} for tid in D: for can in Ck: if can.issubset(tid): if not ssCnt.has_key(can): 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 #构建候选集Ck def aprioriGen(Lk, k): #creates Ck 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: #if first k-2 elements are equal retList.append(Lk[i] | Lk[j]) #set union return retList #得到频繁项集 def apriori(dataSet, minSupport = 0.5): C1 = createC1(dataSet) #构建初始的候选集 D = map(set, dataSet) #将dataSet映射为一个set L1, supportData = scanD(D, C1, minSupport) #得到L1频繁项集 L = [L1] k = 2 while (len(L[k-2]) > 0): Ck = aprioriGen(L[k-2], k) #构建候选集Ck Lk, supK = scanD(D, Ck, minSupport)#scan DB to get Lk supportData.update(supK) L.append(Lk) k += 1 return L, supportData #下面这些函数都是生成关联规则的,如A->B 只要发生A,那么B也会发生的概率称为可信度 #这个是主函数,L是频繁项集合,包含频繁项集合支持度的字典,minConf是最小置信度 def generateRules(L, supportData, minConf=0.7): #supportData is a dict coming from scanD bigRuleList = [] for i in range(1, len(L)):#only get the sets with two or more items for freqSet in L[i]: H1 = [frozenset([item]) for item in freqSet] if (i > 1): rulesFromConseq(freqSet, H1, supportData, bigRuleList, minConf) else: calcConf(freqSet, H1, supportData, bigRuleList, minConf) return bigRuleList #对规则进行评估 def calcConf(freqSet, H, supportData, brl, minConf=0.7): prunedH = [] #create new list to return for conseq in H: conf = supportData[freqSet]/supportData[freqSet-conseq] #calc confidence if conf >= minConf: print (freqSet-conseq,'-->',conseq,'conf:',conf) brl.append((freqSet-conseq, conseq, conf)) prunedH.append(conseq) return prunedH #生成候选规则集合 def rulesFromConseq(freqSet, H, supportData, brl, minConf=0.7): m = len(H[0]) if (len(freqSet) > (m + 1)): #try further merging Hmp1 = aprioriGen(H, m+1)#create Hm+1 new candidates Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf) if (len(Hmp1) > 1): #need at least two sets to merge rulesFromConseq(freqSet, Hmp1, supportData, brl, minConf) def pntRules(ruleList, itemMeaning): for ruleTup in ruleList: for item in ruleTup[0]: print (itemMeaning[item]) print (" -------->") for item in ruleTup[1]: print (itemMeaning[item]) print ("confidence: %f" % ruleTup[2]) print () #print a blank line