机器学习实战--fp-growth

接着前面所学的apriori频繁集挖掘，这里介绍一种更高效的发现频繁集的算法fp-growth(frequence pattern)，对大数据量时尤其有效（近百万条数据中查找，一般电脑只需2s左右）但fp-growth算法只能用来进行发现频繁集，不能挖掘关联规则。

fp-growth算法主要由两步：

1、构建fp树

2、利用fp树发现频繁集

构建fp树：

构建fp树时，需要扫描数据集2次，第一次统计出现的次数，得到项出现的频率；第二次只考虑频繁元素。

1、创建fp树数据结构

class treeNode:
def __init__(self, nameValue, numOccur, parentNode):
self.name = nameValue#元素名
self.count = numOccur#元素出现的次数
self.nodeLink = None
#父节点，后面得到频繁集项进行回溯时会用到
self.parent = parentNode      #needs to be updated
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)

2、除了fp树外，还需要一个头指针table保存指向不同类型的首元素的指针，整个数据结构如下：



第一次遍历数据集会得到各个元素出现的次数，接下来去掉不满足最小支持度的元素项。再进行下一步的构建fp树。再构建时，读入每一个项集并将其添加到一条已经存在的路径中。如果该路径不存在，创建一条新的路径。每个事务就是一个无序集合。假设有集合｛z,x,y｝和｛y,z,x｝,那么再fp树中，相同项只会表示一次。为了解决此问题，在将集合添加到树之前，需要对每个集合进行排序。排序基于元素项的绝对出现频率进行。

#dataSet simple：{feozenset(['r', 'z', 'h', 'j', 'p']):1, frozenset(['z', 'y', 'x', 'w', 'v', 'u', 't', 's']):2}
def createTree(dataSet, minSup=1): #create FP-tree from dataset but don't mine
headerTable = {}
#go over dataSet twice
for trans in dataSet:#first pass counts frequency of occurance
for item in trans:
headerTable[item] = headerTable.get(item, 0) + dataSet[trans]
for k in headerTable.keys():  #remove items not meeting minSup
if headerTable[k] < minSup:
del(headerTable[k])
freqItemSet = set(headerTable.keys())
#print 'freqItemSet: ',freqItemSet
if len(freqItemSet) == 0: return None, None  #if no items meet min support -->get out
for k in headerTable:
headerTable[k] = [headerTable[k], None] #reformat headerTable to use Node link
#print 'headerTable: ',headerTable
retTree = treeNode('Null Set', 1, None) #create tree
for tranSet, count in dataSet.items():  #go through dataset 2nd time
localD = {}
for item in tranSet:  #put transaction items in order
if item in freqItemSet:
localD[item] = headerTable[item][0]
if len(localD) > 0:
orderedItems = [v[0] for v in sorted(localD.items(), key=lambda p: p[1], reverse=True)]
updateTree(orderedItems, retTree, headerTable, count)#populate tree with ordered freq itemset
return retTree, headerTable #return tree and header table

def updateTree(items, inTree, headerTable, count):
if items[0] in inTree.children:#check if orderedItems[0] in retTree.children
inTree.children[items[0]].inc(count) #incrament count
else:   #add items[0] to inTree.children
inTree.children[items[0]] = treeNode(items[0], count, inTree)
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

基于fp树的频繁集的挖掘：

def ascendTree(leafNode, prefixPath): #ascends from leaf node to root
if leafNode.parent != None:
prefixPath.append(leafNode.name)
ascendTree(leafNode.parent, prefixPath)
#treeNode: 理解为headerTable头指针链表中的节点
#创建条件模式基
def findPrefixPath(basePat, treeNode): #treeNode comes from header table
condPats = {}
while treeNode != None:
prefixPath = []
ascendTree(treeNode, prefixPath)
#add the prefixPath count
if len(prefixPath) > 1:
condPats[frozenset(prefixPath[1:])] = treeNode.count
treeNode = treeNode.nodeLink
return condPats
#freqItemList：频繁集项列表
#inTree:fp树
#headerTable:头指针表
def mineTree(inTree, headerTable, minSup, preFix, freqItemList):
bigL = [v[0] for v in sorted(headerTable.items(), key=lambda p: p[1])]#(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)

测试：

#minSup = 3
#simpDat = loadSimpDat()
#initSet = createInitSet(simpDat)
#myFPtree, myHeaderTab = createTree(initSet, minSup)
#myFPtree.disp()
#myFreqList = []
#mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)