机器学习11—Apriori学习笔记

 votesmart下载  https://pypi.python.org/pypi/py-votesmart

test11.py

 

#-*- coding：utf-8
import sys
sys.path.append("apriori.py")

import apriori
from numpy import *

# dataSet = apriori.loadDataSet()
# print("dataSet:")
# print(dataSet)
#
# C1 = apriori.createC1(dataSet)
# print("C1:")
# print(C1)
#
# D = list(map(set, dataSet))
# print("D:")
# print(D)
#
# L1, suppData0 = apriori.scanD(D, C1, 0.5)
# print("L1:")
# print(L1)
# print("suppData0:")
# print(suppData0)
#
#
# L, suppData = apriori.apriori(dataSet)
# print("L:")
# print(L)
#
# L, suppData = apriori.apriori(dataSet, minSupport = 0.5)
# rules = apriori.generateRules(L, suppData, minConf = 0.5)
# print("L:")
# print(L)
# print("rules:")
# print(rules)



mushDatSet = [line.split() for line in open('mushroom.dat').readlines()]
L, suppData = apriori.apriori(mushDatSet, minSupport = 0.3)
print("L[1]:")
print(L[1])
for item in L[1]:
    if item.intersection('2'):
        print(item)



print("over!!!")

 

apriori.py

'''
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 list(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:
            if can.issubset(tid):
                if can not 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 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]
            test0 = list(Lk[i])
            test1 = list(Lk[j])
            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):
    C1 = createC1(dataSet)
    D = list(map(set, dataSet))
    L1, supportData = scanD(D, C1, minSupport)
    L = [L1]
    k = 2
    test0 = L[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 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