更行决策树的部分内容

src/python/12.FrequentPattemTree/apriori.py

@@ -1,12 +0,0 @@
-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 map(frozenset,c1)
-def scanD(D,ck,minSupport):
-    ssCnt = {}

src/python/12.FrequentPattemTree/fpGrowth.py

@@ -1,19 +1,178 @@
+'''
+Created on Jun 14, 2011
+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/片刻
+'''
+print(__doc__)
+
+
 class treeNode:
-    def __init__(self,nameValue,numOccur,parentNode):
+    def __init__(self, nameValue, numOccur, parentNode):
         self.name = nameValue
         self.count = numOccur
         self.nodeLink = None
+        # needs to be updated
         self.parent = parentNode
         self.children = {}
-    def inc(self,numOccur):
+
+    def inc(self, numOccur):
         self.count += numOccur
-    def disp(self,ind=1):
-        print(' '*ind,self.name,' ',self.count)
+
+    def disp(self, ind=1):
+        print '  '*ind, self.name, ' ', self.count
         for child in self.children.values():
             child.disp(ind+1)
 
-    if __name__ == "__main__":
-        import fpGrowth
-        rootNode = fpGrowth.treeNode('pyramid',9,None)
-        rootNode.children['eye']=fpGrowth.treeNode('eye',13,None)
-        rootNode.disp()
+
+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
+
+
+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 = [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)
+
+
+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 simpDat
+
+
+def createInitSet(dataSet):
+    retDict = {}
+    for trans in dataSet:
+        retDict[frozenset(trans)] = 1
+    return retDict
+
+
+import twitter
+from time import sleep
+import re
+
+
+def textParse(bigString):
+    urlsRemoved = re.sub('(http:[/][/]|www.)([a-z]|[A-Z]|[0-9]|[/.]|[~])*', '', bigString)    
+    listOfTokens = re.split(r'\W*', urlsRemoved)
+    return [tok.lower() for tok in listOfTokens if len(tok) > 2]
+
+
+def getLotsOfTweets(searchStr):
+    CONSUMER_KEY = ''
+    CONSUMER_SECRET = ''
+    ACCESS_TOKEN_KEY = ''
+    ACCESS_TOKEN_SECRET = ''
+    api = twitter.Api(consumer_key=CONSUMER_KEY, consumer_secret=CONSUMER_SECRET,
+                      access_token_key=ACCESS_TOKEN_KEY, 
+                      access_token_secret=ACCESS_TOKEN_SECRET)
+    #you can get 1500 results 15 pages * 100 per page
+    resultsPages = []
+    for i in range(1,15):
+        print "fetching page %d" % i
+        searchResults = api.GetSearch(searchStr, per_page=100, page=i)
+        resultsPages.append(searchResults)
+        sleep(6)
+    return resultsPages
+
+
+def mineTweets(tweetArr, minSup=5):
+    parsedList = []
+    for i in range(14):
+        for j in range(100):
+            parsedList.append(textParse(tweetArr[i][j].text))
+    initSet = createInitSet(parsedList)
+    myFPtree, myHeaderTab = createTree(initSet, minSup)
+    myFreqList = []
+    mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)
+    return myFreqList
+
+
+#minSup = 3
+#simpDat = loadSimpDat()
+#initSet = createInitSet(simpDat)
+#myFPtree, myHeaderTab = createTree(initSet, minSup)
+#myFPtree.disp()
+#myFreqList = []
+#mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)