FP-growth算法的python實現
- 2019 年 10 月 30 日
- 筆記
FP-growth算法是一種用於發現數據集中頻繁模式的有效方法。Apriori算法在產生頻繁模式完全集前需要對數據庫進行多次掃描，同時產生大量的候選頻繁集，這就使Apriori算法時間和空間複雜度較大。FP-growth算法由Apriori算法產生候選項集，然後掃描數據集來檢查它們是否頻繁。由於只對數據集掃描兩次，因此它比Apriori算法速度要快，通常性能要好兩個數量級以上。
在FP-growth算法中，數據集存儲在一個稱為FP(Frequent Pattern)樹的結構中。FP樹構建完成後，可以通過查找元素項的條件基以及構建條件FP樹來發現頻繁集。該過程不斷以更多的元素為條件重複進行，知道FP樹只包含一個元素為止。
下面僅以這個簡單的數據集為例子–實際上，既使在多達百萬條記錄的大數據集上，FP-growth算法也能快速運行。
python代碼：
''' FP-Growth FP means frequent pattern the FP-Growth algorithm needs: 1. FP-tree (class treeNode) 2. header table (use dict) This finds frequent itemsets similar to apriori but does not find association rules. @author: Peter ''' def loadSimpDat(): simpDat = [['r', 'z', 'h', 'j', 'p'], ['z', 'y', 'x', 'w', 'v', 'u', 't', 's'], ['z','p','x'], ['r', 'x', 'n', 'o', 's'], ['y', 'r', 'x', 'z', 'q', 't', 'p'], ['y', 'z', 'x', 'e', 'q', 's', 't', 'm']] return simpDat class treeNode: def __init__(self, nameValue, numOccur, parentNode): self.name = nameValue self.count = numOccur self.nodeLink = None 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) #def __lt__(self, other):#定義 "<"用於sorted() #return self.count < other.count 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 list(headerTable.keys()): #remove items not meeting minSup if headerTable[k] < minSup: headerTable.pop(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 def ascendTree(leafNode, prefixPath): #ascends from leaf node to root if leafNode.parent != None: prefixPath.append(leafNode.name) ascendTree(leafNode.parent, prefixPath) def findPrefixPath(basePat, treeNode): #treeNode comes from header table condPats = {} while treeNode != None: prefixPath = [] ascendTree(treeNode, prefixPath) if len(prefixPath) > 1: condPats[frozenset(prefixPath[1:])] = treeNode.count treeNode = treeNode.nodeLink return condPats def mineTree(inTree, headerTable, minSup, preFix, freqItemList): bigL = [k for k,v in sorted(headerTable.items(), key=lambda p: p[1][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)#遞歸 def createInitSet(dataSet): retDict = {} for trans in dataSet: retDict[frozenset(trans)] = 1 return retDict minSup = 4 simpDat = loadSimpDat() initSet = createInitSet(simpDat) myFPtree, myHeaderTab = createTree(initSet, minSup) myFreqList = [] if myFPtree is not None: myFPtree.disp() mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList) print("支持度為%d時，頻繁項數為%d:"%(minSup, len(myFreqList))) print("頻繁項集為:") for item in myFreqList: print(item)
