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https://github.com/apachecn/ailearning.git
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更新完2个关于Apriori所有的项目案例
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jiangzhonglian
@@ -87,7 +87,7 @@ def aprioriGen(Lk, k):
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# if first k-2 elements are equal
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if L1 == L2:
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# set union
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print 'union=', Lk[i] | Lk[j], Lk[i], Lk[j]
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# print 'union=', Lk[i] | Lk[j], Lk[i], Lk[j]
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retList.append(Lk[i] | Lk[j])
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return retList
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@@ -108,7 +108,7 @@ def apriori(dataSet, minSupport=0.5):
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# 计算支持support， L1表示满足support的key, supportData表示全集的集合
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L1, supportData = scanD(D, C1, minSupport)
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print "L1=", L1, "\n", "outcome: ", supportData
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# print "L1=", L1, "\n", "outcome: ", supportData
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L = [L1]
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k = 2
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@@ -176,7 +176,7 @@ def rulesFromConseq(freqSet, H, supportData, brl, minConf=0.7):
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Hmp1 = aprioriGen(H, m+1)
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Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf)
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# 如果有2个结果都可以，直接返回结果就行，下面这个判断是多余，我个人觉得
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print 'Hmp1=', Hmp1
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# print 'Hmp1=', Hmp1
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if (len(Hmp1) > 1):
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# print '-------'
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# print len(freqSet), len(Hmp1[0]) + 1
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@@ -208,108 +208,117 @@ def generateRules(L, supportData, minConf=0.7):
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return bigRuleList
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def getActionIds():
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from time import sleep
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from votesmart import votesmart
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# votesmart.apikey = 'get your api key first'
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votesmart.apikey = 'a7fa40adec6f4a77178799fae4441030'
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actionIdList = []
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billTitleList = []
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fr = open('testData/Apriori_recent20bills.txt')
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for line in fr.readlines():
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billNum = int(line.split('\t')[0])
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try:
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billDetail = votesmart.votes.getBill(billNum) # api call
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for action in billDetail.actions:
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if action.level == 'House' and (action.stage == 'Passage' or action.stage == 'Amendment Vote'):
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actionId = int(action.actionId)
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print 'bill: %d has actionId: %d' % (billNum, actionId)
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actionIdList.append(actionId)
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billTitleList.append(line.strip().split('\t')[1])
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except:
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print "problem getting bill %d" % billNum
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sleep(1) # delay to be polite
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return actionIdList, billTitleList
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def getTransList(actionIdList, billTitleList): #this will return a list of lists containing ints
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itemMeaning = ['Republican', 'Democratic']#list of what each item stands for
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for billTitle in billTitleList:#fill up itemMeaning list
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itemMeaning.append('%s -- Nay' % billTitle)
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itemMeaning.append('%s -- Yea' % billTitle)
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transDict = {}#list of items in each transaction (politician)
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voteCount = 2
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for actionId in actionIdList:
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sleep(3)
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print 'getting votes for actionId: %d' % actionId
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try:
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voteList = votesmart.votes.getBillActionVotes(actionId)
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for vote in voteList:
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if not transDict.has_key(vote.candidateName):
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transDict[vote.candidateName] = []
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if vote.officeParties == 'Democratic':
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transDict[vote.candidateName].append(1)
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elif vote.officeParties == 'Republican':
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transDict[vote.candidateName].append(0)
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if vote.action == 'Nay':
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transDict[vote.candidateName].append(voteCount)
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elif vote.action == 'Yea':
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transDict[vote.candidateName].append(voteCount + 1)
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except:
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print "problem getting actionId: %d" % actionId
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voteCount += 2
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return transDict, itemMeaning
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# 暂时没用上
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# def pntRules(ruleList, itemMeaning):
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# for ruleTup in ruleList:
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# for item in ruleTup[0]:
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# print itemMeaning[item]
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# print " -------->"
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# for item in ruleTup[1]:
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# print itemMeaning[item]
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# print "confidence: %f" % ruleTup[2]
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# print #print a blank line
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def main():
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# 以前的测试
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# project_dir = os.path.dirname(os.path.dirname(os.getcwd()))
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# 1.收集并准备数据
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# 收集并准备数据
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# dataMat, labelMat = loadDataSet("%s/resources/Apriori_testdata.txt" % project_dir)
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# 1. 加载数据
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dataSet = loadDataSet()
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print(dataSet)
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# 调用 apriori 做购物篮分析
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# 支持度满足阈值的key集合L，和所有key的全集suppoerData
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L, supportData = apriori(dataSet, minSupport=0.5)
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# print L, supportData
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print '\ngenerateRules\n'
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generateRules(L, supportData, minConf=0.05)
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# 现在的的测试
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# # 1. 加载数据
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# dataSet = loadDataSet()
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# print(dataSet)
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# # 调用 apriori 做购物篮分析
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# # 支持度满足阈值的key集合L，和所有key的全集suppoerData
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# L, supportData = apriori(dataSet, minSupport=0.5)
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# # print L, supportData
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# print '\ngenerateRules\n'
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# rules = generateRules(L, supportData, minConf=0.05)
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# print rules
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# 项目实战
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# 构建美国国会投票记录的事务数据集
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# actionIdList, billTitleList = getActionIds()
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# # 测试前2个
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# # transDict, itemMeaning = getTransList(actionIdList[: 2], billTitleList[: 2])
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# # transDict 表示 action_id的集合，transDict[key]这个就是action_id对应的选项，例如 [1, 2, 3]
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# transDict, itemMeaning = getTransList(actionIdList, billTitleList)
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# # 得到全集的数据
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# dataSet = [transDict[key] for key in transDict.keys()]
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# L, supportData = apriori(dataSet, minSupport=0.3)
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# rules = generateRules(L, supportData, minConf=0.95)
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# print rules
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# 项目实战
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# 发现毒蘑菇的相似特性
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# 得到全集的数据
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dataSet = [line.split() for line in open("testData/Apriori_mushroom.dat").readlines()]
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L, supportData = apriori(dataSet, minSupport=0.3)
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# 2表示毒蘑菇，1表示可食用的蘑菇
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# 找出关于2的频繁子项出来，就知道如果是毒蘑菇，那么出现频繁的也可能是毒蘑菇
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for item in L[1]:
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if item.intersection('2'):
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print item
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for item in L[2]:
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if item.intersection('2'):
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print item
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if __name__ == "__main__":
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main()
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def pntRules(ruleList, itemMeaning):
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for ruleTup in ruleList:
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for item in ruleTup[0]:
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print itemMeaning[item]
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print " -------->"
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for item in ruleTup[1]:
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print itemMeaning[item]
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print "confidence: %f" % ruleTup[2]
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print #print a blank line
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# from time import sleep
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# from votesmart import votesmart
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# votesmart.apikey = 'a7fa40adec6f4a77178799fae4441030'
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# #votesmart.apikey = 'get your api key first'
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# def getActionIds():
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# actionIdList = []; billTitleList = []
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# fr = open('recent20bills.txt')
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# for line in fr.readlines():
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# billNum = int(line.split('\t')[0])
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# try:
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# billDetail = votesmart.votes.getBill(billNum) #api call
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# for action in billDetail.actions:
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# if action.level == 'House' and \
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# (action.stage == 'Passage' or action.stage == 'Amendment Vote'):
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# actionId = int(action.actionId)
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# print 'bill: %d has actionId: %d' % (billNum, actionId)
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# actionIdList.append(actionId)
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# billTitleList.append(line.strip().split('\t')[1])
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# except:
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# print "problem getting bill %d" % billNum
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# sleep(1) #delay to be polite
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# return actionIdList, billTitleList
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#
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# def getTransList(actionIdList, billTitleList): #this will return a list of lists containing ints
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# itemMeaning = ['Republican', 'Democratic']#list of what each item stands for
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# for billTitle in billTitleList:#fill up itemMeaning list
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# itemMeaning.append('%s -- Nay' % billTitle)
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# itemMeaning.append('%s -- Yea' % billTitle)
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# transDict = {}#list of items in each transaction (politician)
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# voteCount = 2
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# for actionId in actionIdList:
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# sleep(3)
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# print 'getting votes for actionId: %d' % actionId
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# try:
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# voteList = votesmart.votes.getBillActionVotes(actionId)
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# for vote in voteList:
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# if not transDict.has_key(vote.candidateName):
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# transDict[vote.candidateName] = []
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# if vote.officeParties == 'Democratic':
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# transDict[vote.candidateName].append(1)
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# elif vote.officeParties == 'Republican':
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# transDict[vote.candidateName].append(0)
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# if vote.action == 'Nay':
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# transDict[vote.candidateName].append(voteCount)
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# elif vote.action == 'Yea':
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# transDict[vote.candidateName].append(voteCount + 1)
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# except:
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# print "problem getting actionId: %d" % actionId
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# voteCount += 2
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# return transDict, itemMeaning
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