机器学习之使用FP-growth算法来高效发现频繁项集

    本文主要记录本人在学习机器学习过程中的相关代码实现，参考《机器学习实战》

from numpy import *#构造简单测试数据def loadSimpDat():    simpDat = [['r', 'z', 'h', 'j', 'p'],               ['z', 'y', 'x', 'w', 'v', 'u', 't', 's'],               ['z'],               ['r', 'x', 'n', 'o', 's'],               ['y', 'r', 'x', 'z', 'q', 't', 'p'],               ['y', 'z', 'x', 'e', 'q', 's', 't', 'm']]    return simpDatdef createInitSet(dataSet):    retDict = {}    for trans in dataSet:        retDict[frozenset(trans)] = 1    return retDict#FP树的类定义class treeNode:    def __init__(self,nameValue,numOccur,parentNode):        self.name=nameValue        self.count=numOccur        self.nodeLink=None#用于链接相似的元素项        self.parent=parentNode#指向当前节点的父节点        self.children={}#当前节点字节点            def inc(self,numOccur):        self.count+=numOccur    def disp(self,ind=1):        print('  '*ind,self.name,'  ',self.count)        for child in self.children.values():            child.disp(ind+1)#~ 测试#~ rootNode=treeNode('pyramid',9,None)#~ rootNode.children['eye']=treeNode('eye',13,None)#~ rootNode.children['phoenix']=treeNode('phoenix',3,None)#~ rootNode.disp()#FP树构建函数,数据集以及最小支持度作为参数来构建FP树def createTree(dataSet,minSup=1):    headerTable={}#用于保存FT树的头表    for trans in dataSet:        for item in trans:            headerTable[item]=headerTable.get(item,0)+dataSet[trans]    for k in list(headerTable.keys()):        #~ print(k,headerTable[k])        if headerTable[k]<minSup:            del(headerTable[k])    freqItemSet=set(headerTable.keys())    #~ print(freqItemSet)    if len(freqItemSet)==0: return None,None    for k in headerTable:        headerTable[k]=[headerTable[k],None]#None之后用于指向下一个同类节点    retTree =treeNode('Null Set',1,None)    for tranSet,count in dataSet.items():        localD={}        for item in tranSet:            if item in freqItemSet:                localD[item]=headerTable[item][0]        if len(localD)>0:            orderedItem=[v[0] for v in sorted(localD.items(),                key=lambda p:p[1],reverse=True)]            updateTree(orderedItem,retTree,headerTable,count)    return retTree,headerTable#树生长函数def updateTree(items,inTree,headerTable,count):    if items[0] in inTree.children:        inTree.children[items[0]].inc(count)    else:        inTree.children[items[0]]=treeNode(items[0],count,inTree)        if headerTable[items[0]][1]==None:            headerTable[items[0]][1]=inTree.children[items[0]]        else:            updateHeader(headerTable[items[0]][1],inTree.children[items[0]])    if len(items)>1:        updateTree(items[1::],inTree.children[items[0]],headerTable,count)#更新头表def updateHeader(nodeToTest, targetNode):   #this version does not use recursion    while (nodeToTest.nodeLink != None):    #Do not use recursion to traverse a linked list!        nodeToTest = nodeToTest.nodeLink    nodeToTest.nodeLink = targetNode#~ 测试#~ simpDat=loadSimpDat()#~ print(simpDat)#~ initSet=createInitSet(simpDat)#~ print(initSet)#~ myFPtree,myHeaderTab=createTree(initSet,3)#~ myFPtree.disp()#~ 发现以给定元素项结尾的所有路径的函数def ascendTree(leafNode,prefixPath):    if leafNode.parent!=None:        prefixPath.append(leafNode.name)        ascendTree(leafNode.parent,prefixPath)def findPrefixPath(basePat,treeNode):    condPats={}    while treeNode !=None:        prefixPath=[]        ascendTree(treeNode,prefixPath)        if len(prefixPath)>1:            condPats[frozenset(prefixPath[1:])]=treeNode.count        treeNode=treeNode.nodeLink    return condPats#~ simpDat=loadSimpDat()#~ initSet=createInitSet(simpDat)#~ myFPtree,myHeaderTab=createTree(initSet,3)#~ myFPtree.disp()#~ condPats=findPrefixPath('r',myHeaderTab['r'][1])#~ print(condPats)#~ 递归查找频繁项集的mineTree函数def mineTree(inTree, headerTable, minSup, preFix, freqItemList):    localD = {}    for item in headerTable.keys():        localD[item]=headerTable[item][0]    bigL=[v[0] for v in sorted(localD.items(),        key=lambda p:p[1])]    #~ bigL = [v[0] for v in sorted(headerTable.items(), key=lambda p: p[0])]#(sort header table)    for basePat in bigL:  #start from bottom of header table        newFreqSet = preFix.copy()        newFreqSet.add(basePat)        #~ print('finalFrequent Item: ',newFreqSet)    #append to set        freqItemList.append(newFreqSet)        condPattBases = findPrefixPath(basePat, headerTable[basePat][1])        #print 'condPattBases :',basePat, condPattBases        #2. construct cond FP-tree from cond. pattern base        myCondTree, myHead = createTree(condPattBases, minSup)        #print 'head from conditional tree: ', myHead        if myHead != None: #3. mine cond. FP-tree            #~ print('conditional tree for: ',newFreqSet)            #~ myCondTree.disp(1)                        mineTree(myCondTree, myHead, minSup, newFreqSet, freqItemList)#~ simpDat=loadSimpDat()#~ initSet=createInitSet(simpDat)#~ myFPtree,myHeaderTab=createTree(initSet,3)#~ freqItems=[]#~ mineTree(myFPtree,myHeaderTab,3,set([]),freqItems)parsedDat=[line.split() for line in open('kosarak.dat').readlines()]initSet=createInitSet(parsedDat)myFPtree,myHeaderTab=createTree(initSet,100000)myFreqList=[]mineTree(myFPtree,myHeaderTab,100000,set([]),myFreqList)print(len(myFreqList))print(myFreqList)