ailearning/src/python/apriori.py

#!/usr/bin/python
# coding: utf8

'''
Created on Mar 24, 2011
Ch 11 code
@author: Peter
'''
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:
                C1.append([item])
                
    C1.sort()
    return map(frozenset, C1) # use frozen set so we
                              # can use it as a key in a dict

def scanD(D, Ck, minSupport):
    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
    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 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

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, supportData = scanD(D, C1, minSupport)
    print("outcome: ", supportData)

    L = [L1]
    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)
        k += 1
    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/testSet.txt" % project_dir)


    # 1. 加载数据
    dataSet = loadDataSet()
    print(dataSet)
    # 调用 apriori 做购物篮分析
    apriori(dataSet, minSupport = 0.7)

if __name__=="__main__":
    main()






























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