头歌平台第六章关联挖掘实验二：FP-growth
第一关：构建FP-tree
def loadSimpDat():#加载数据集
simpDat = [['beer', 'milk', 'chicken'], ['milk', 'bread'], ['milk', 'diaper'],
['beer', 'milk', 'bread'], ['beer', 'diaper'], ['milk', 'diaper'],
['beer', 'diaper'], ['beer', 'milk', 'diaper', 'chicken'], ['beer', 'milk', 'diaper']]
return simpDat
def createInitSet(dataSet):#处理数据集，化为 （记录，计数）的形式
retDict = {}
for trans in dataSet:
fset = frozenset(trans)
retDict.setdefault(fset, 0)
retDict[fset] += 1
return retDict
class treeNode:#构造树节点
def __init__(self, nameValue, numOccur, parentNode):
self.name = nameValue
self.count = numOccur
self.nodeLink = None
self.parent = parentNode
self.children = {}
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)
def createTree(dataSet, minSup=1):
headerTable = {}
#此一次遍历数据集， 记录每个数据项的支持度,存在headerTable中
for trans in dataSet:
for item in trans:
headerTable[item] = headerTable.get(item, 0) + 1
#根据最小支持度过滤
lessThanMinsup = list(filter(lambda k:headerTable[k] < minSup, headerTable.keys()))
for k in lessThanMinsup: del(headerTable[k])
freqItemSet = set(headerTable.keys())
#如果所有数据都不满足最小支持度，返回None, None
if len(freqItemSet) == 0:
return None, None
for k in headerTable:
headerTable[k] = [headerTable[k], None]
#初始化FP树，即创建根节点null
retTree = treeNode('φ', 1, None)
#print (headerTable)
#第二次遍历数据集，构建fp-tree
for tranSet, count in dataSet.items():
#根据最小支持度处理一条训练样本，key:样本中的一个样例，value:该样例的的全局支持度
localD = {}
#遍历这条数据中的每个元素
#过滤每条记录中支持度小于最小支持度的元素
#把headerTable中记录的该元素的出现次数赋值给localD中的对应元素
#********** Begin **********#
for item in tranSet:
if item in freqItemSet:
localD[item]=headerTable[item]
#********** End **********#
if len(localD) > 0:
#根据全局频繁项对每个事务中的数据进行排序,等价于 order by p[1] desc, p[0] desc
orderedItems = [v[0] for v in sorted(localD.items(), key=lambda p: (p[1],p[0]), reverse=True)]
#print (orderedItems)
updateTree(orderedItems, retTree, headerTable, count)
return retTree, headerTable
#根据items中的项往FP树中插入节点
def updateTree(items, inTree, headerTable, count):
# 判断items的第一项是否已作为根节点null的子结点,是的话增加计数
#********** Begin **********#
if items[0] in inTree.children:
inTree.children[items[0]].inc(count)
#********** End **********#
#不是子结点则创建新分支，并将headerTable的指针更新（更新代码已给出）
#********** Begin **********#
else:
inTree.children[items[0]]=treeNode(items[0],count,inTree)
#********** End **********#
if headerTable[items[0]][1] == None: # update header table
headerTable[items[0]][1] = inTree.children[items[0]]
else:
updateHeader(headerTable[items[0]][1], inTree.children[items[0]])
if len(items) > 1: # call updateTree() with remaining ordered items
updateTree(items[1:], inTree.children[items[0]], headerTable, count)
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
simpDat = loadSimpDat()
dictDat = createInitSet(simpDat)
myFPTree,myheader = createTree(dictDat, 3)
myFPTree.disp()
第二关：从FP数中挖掘频繁项集
class treeNode:
def __init__(self, nameValue, numOccur, parentNode):
self.name = nameValue
self.count = numOccur
self.nodeLink = None
self.parent = parentNode
self.children = {}
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)
def updateHeader(nodeToTest, targetNode):
while nodeToTest.nodeLink != None:
nodeToTest = nodeToTest.nodeLink
nodeToTest.nodeLink = targetNode
def updateFPtree(items, inTree, headerTable, count):
if items[0] in inTree.children:
# 判断items的第一个结点是否已作为子结点
inTree.children[items[0]].inc(count)
else:
# 创建新的分支
inTree.children[items[0]] = treeNode(items[0], count, inTree)
# 更新相应频繁项集的链表，往后添加
if headerTable[items[0]][1] == None:
headerTable[items[0]][1] = inTree.children[items[0]]
else:
updateHeader(headerTable[items[0]][1], inTree.children[items[0]])
# 递归
if len(items) > 1:
updateFPtree(items[1::], inTree.children[items[0]], headerTable, count)
def createFPtree(dataSet, minSup=1):
headerTable = {}
for trans in dataSet:
for item in trans:
headerTable[item] = headerTable.get(item, 0) + dataSet[trans]
for k in list(headerTable):#py2.7可写作for k in headerTable.keys() 此处为py3.x
if headerTable[k] < minSup:
del(headerTable[k]) # 删除不满足最小支持度的元素
freqItemSet = set(headerTable.keys()) # 满足最小支持度的频繁项集
if len(freqItemSet) == 0:
return None, None
for k in headerTable:
headerTable[k] = [headerTable[k], None] # element: [count, node]
retTree = treeNode('Null Set', 1, None)
#print (headerTable)
for tranSet, count in dataSet.items():
# dataSet：[element, count]
localD = {}
for item in tranSet:
if item in freqItemSet: # 过滤，只取该样本中满足最小支持度的频繁项
localD[item] = headerTable[item][0] # element : count
if len(localD) > 0:
# 根据全局频数从大到小对单样本排序
orderedItem = [v[0] for v in sorted(localD.items(), key=lambda p: (p[1],p[0]), reverse=True)]
#print (orderedItem)
# 用过滤且排序后的样本更新树
updateFPtree(orderedItem, retTree, headerTable, count)
return retTree, headerTable
# 数据集
def loadSimpDat():
simDat = [['beer', 'milk', 'chicken'], ['milk', 'bread'], ['milk', 'diaper'],
['beer', 'milk', 'bread'], ['beer', 'diaper'], ['milk', 'diaper'],
['beer', 'diaper'], ['beer', 'milk', 'diaper', 'chicken'], ['beer', 'milk', 'diaper']]
return simDat
# 构造成 element : count 的形式
def createInitSet(dataSet):
retDict={}
for trans in dataSet:
key = frozenset(trans)
if key in retDict:
retDict[frozenset(trans)] += 1
else:
retDict[frozenset(trans)] = 1
return retDict
# 递归回溯
def ascendFPtree(leafNode, prefixPath):
if leafNode.parent != None:
prefixPath.append(leafNode.name)
ascendFPtree(leafNode.parent, prefixPath)
# 条件模式基
def findPrefixPath(basePat, myHeaderTab):
treeNode = myHeaderTab[basePat][1] # basePat在FP树中的第一个结点
condPats = {}
#当treenode还存在
#声明变量前缀路径
#调用递归回溯函数
#前缀存在则为其在condPats的对应项计数，为当前treenode的计数值
#treenode跳转为当前项在FP树的另一个节点
#********** Begin **********#
while treeNode != None:
prefixPath = []
ascendFPtree(treeNode, prefixPath)
if len(prefixPath) > 1:
condPats[frozenset(prefixPath[1:])] = treeNode.count
treeNode = treeNode.nodeLink
#********** End **********#
return condPats
def mineFPtree(inTree, headerTable, minSup, preFix, freqItemList):#挖掘条件FP树
# 最开始的频繁项集是headerTable中的各元素
bigL = [v[0] for v in sorted(headerTable.items(), key=lambda p:str(p[1]))] # 根据频繁项的总频次排序
#print ('bigL:',bigL)
for basePat in bigL: # 对每个频繁项
newFreqSet = preFix.copy()
newFreqSet.add(basePat)
listFreqSet=sorted(list(newFreqSet),key= lambda i:i[0])
#print ('当前频繁项集：',newFreqSet)
freqItemList.append(listFreqSet)
#获得当前频繁项集的条件模式基
#构造当前频繁项集的FP树
#当前项集的headtable还有项则递归挖掘条件FP树
#********** Begin **********#
condPattBases=findPrefixPath(basePat,headerTable)
myCondTree,myHead=createFPtree(condPattBases,minSup)
if myHead!=None:
mineFPtree(myCondTree,myHead,minSup,newFreqSet,freqItemList)
#********** End **********#
simpDat=loadSimpDat()
initSet=createInitSet(simpDat)
retTree, headerTable=createFPtree(initSet,3)
retTree.disp()
freqItems=[]
#print ('headtable:',headerTable)
mineFPtree(retTree,headerTable,3,set([]),freqItems)
print (freqItems)
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