更新完apriori的频繁子项的代码

src/python/11.Apriori/apriori.py

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+#!/usr/bin/python
+# coding: utf8
+
+'''
+Created on Mar 24, 2011
+Update on 2017-03-16
+Ch 11 code
+@author: Peter/片刻
+'''
+print(__doc__)
+from numpy import *
+
+
+def loadDataSet():
+    return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]
+
+
+def createC1(dataSet):
+    C1 = []
+    for transaction in dataSet:
+        for item in transaction:
+            if not [item] in C1:
+                # 遍历所有的元素，然后append到C1中
+                C1.append([item])
+    # 对数组进行 从小到大 的排序
+    C1.sort()
+    # frozenset表示冻结的set集合，元素无可改变；可以把它当字典的key来使用
+    return map(frozenset, C1)
+
+
+def scanD(D, Ck, minSupport):
+    # 临时存放，查看Ck每个元素 并 计算元素出现的次数 生成相应的字典
+    # D用来判断，CK中的元素，是否存在于原数据D中
+    ssCnt = {}
+    for tid in D:
+        for can in Ck:
+            # s.issubset(t)  测试是否 s 中的每一个元素都在 t 中
+            if can.issubset(tid):
+                if not ssCnt.has_key(can):
+                    ssCnt[can] = 1
+                else:
+                    ssCnt[can] += 1
+    # 元素有多少行
+    numItems = float(len(D))
+    retList = []
+    supportData = {}
+    for key in ssCnt:
+        # 计算支持度
+        support = ssCnt[key]/numItems
+        if support >= minSupport:
+            # 在retList的首位插入元素，只存储支持度满足频繁项集的值
+            retList.insert(0, key)
+        # 存储所有的key和对应的support值
+        supportData[key] = support
+    return retList, supportData
+
+
+# creates Ck
+def aprioriGen(Lk, k):
+    """aprioriGen(循环数据集，然后进行两两合并)
+
+    Args:
+        Lk 频繁项集
+        k 元素的前k-2相同，就进行合并
+    Returns:
+        retList 元素两两合并的数据集
+    """
+    retList = []
+    lenLk = len(Lk)
+    # 循环Lk这个数组
+    for i in range(lenLk):
+        for j in range(i+1, lenLk):
+            L1 = list(Lk[i])[: k-2]
+            L2 = list(Lk[j])[: k-2]
+            # print '-----', Lk, Lk[i], L1
+            L1.sort()
+            L2.sort()
+            # 第一次L1,L2为空，元素直接进行合并，返回元素两两合并的数据集
+            # if first k-2 elements are equal
+            if L1 == L2:
+                # set union
+                retList.append(Lk[i] | Lk[j])
+    return retList
+
+
+def apriori(dataSet, minSupport=0.5):
+    # 冻结每一行数据
+    C1 = createC1(dataSet)
+    D = map(set, dataSet)
+
+    # 计算支持support， L1表示满足support的key, supportData表示全集的集合
+    L1, supportData = scanD(D, C1, minSupport)
+    # print "L1=", L1, "\n", "outcome: ", supportData
+
+    L = [L1]
+    k = 2
+    while (len(L[k-2]) > 0):
+        # print 'L[k-2]=', L[k-2], k
+        Ck = aprioriGen(L[k-2], k)
+        # print 'Ck=', Ck
+        # can DB to get Lk
+        Lk, supK = scanD(D, Ck, minSupport)
+        supportData.update(supK)
+        # L元素在增加
+        L.append(Lk)
+        k += 1
+        # print 'k=', k, len(L[k-2])
+    return L, supportData
+
+
+def main():
+    # project_dir = os.path.dirname(os.path.dirname(os.getcwd()))
+    # 1.收集并准备数据
+    # dataMat, labelMat = loadDataSet("%s/resources/Apriori_testdata.txt" % project_dir)
+
+    # 1. 加载数据
+    dataSet = loadDataSet()
+    print(dataSet)
+    # 调用 apriori 做购物篮分析
+    L, supportData = apriori(dataSet, minSupport=0.7)
+    print L, supportData
+
+
+if __name__ == "__main__":
+    main()
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+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         
+
+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
+
+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
+        
+            
+# from time import sleep
+# from votesmart import votesmart
+# votesmart.apikey = 'a7fa40adec6f4a77178799fae4441030'
+# #votesmart.apikey = 'get your api key first'
+# def getActionIds():
+#     actionIdList = []; billTitleList = []
+#     fr = open('recent20bills.txt')
+#     for line in fr.readlines():
+#         billNum = int(line.split('\t')[0])
+#         try:
+#             billDetail = votesmart.votes.getBill(billNum) #api call
+#             for action in billDetail.actions:
+#                 if action.level == 'House' and \
+#                 (action.stage == 'Passage' or action.stage == 'Amendment Vote'):
+#                     actionId = int(action.actionId)
+#                     print 'bill: %d has actionId: %d' % (billNum, actionId)
+#                     actionIdList.append(actionId)
+#                     billTitleList.append(line.strip().split('\t')[1])
+#         except:
+#             print "problem getting bill %d" % billNum
+#         sleep(1)                                      #delay to be polite
+#     return actionIdList, billTitleList
+#
+# def getTransList(actionIdList, billTitleList): #this will return a list of lists containing ints
+#     itemMeaning = ['Republican', 'Democratic']#list of what each item stands for
+#     for billTitle in billTitleList:#fill up itemMeaning list
+#         itemMeaning.append('%s -- Nay' % billTitle)
+#         itemMeaning.append('%s -- Yea' % billTitle)
+#     transDict = {}#list of items in each transaction (politician)
+#     voteCount = 2
+#     for actionId in actionIdList:
+#         sleep(3)
+#         print 'getting votes for actionId: %d' % actionId
+#         try:
+#             voteList = votesmart.votes.getBillActionVotes(actionId)
+#             for vote in voteList:
+#                 if not transDict.has_key(vote.candidateName):
+#                     transDict[vote.candidateName] = []
+#                     if vote.officeParties == 'Democratic':
+#                         transDict[vote.candidateName].append(1)
+#                     elif vote.officeParties == 'Republican':
+#                         transDict[vote.candidateName].append(0)
+#                 if vote.action == 'Nay':
+#                     transDict[vote.candidateName].append(voteCount)
+#                 elif vote.action == 'Yea':
+#                     transDict[vote.candidateName].append(voteCount + 1)
+#         except:
+#             print "problem getting actionId: %d" % actionId
+#         voteCount += 2
+#     return transDict, itemMeaning