更行决策树的部分内容

src/python/03.DecisionTree/DecisionTree.py

@@ -20,8 +20,6 @@ def createDataSet():
         无需传入参数
     Returns:
         返回数据集和对应的label标签
-    Raises:
-
     """
     dataSet = [[1, 1, 'yes'],
                [1, 1, 'yes'],
@@ -44,9 +42,7 @@ def calcShannonEnt(dataSet):
     Args:
         dataSet 数据集
     Returns:
-        返回香农熵的计算值
-    Raises:
-
+        返回 每一组feature下的某个分类下，香农熵的信息期望
     """
     # 求list的长度，表示计算参与训练的数据量
     numEntries = len(dataSet)
@@ -81,8 +77,6 @@ def splitDataSet(dataSet, axis, value):
         value 表示axis列对应的value值
     Returns:
         axis列为value的数据集【该数据集需要排除axis列】
-    Raises:
-
     """
     retDataSet = []
     for featVec in dataSet:
@@ -106,10 +100,8 @@ def chooseBestFeatureToSplit(dataSet):
         dataSet 数据集
     Returns:
         bestFeature 最优的特征列
-    Raises:
-
     """
-    # 求第一行有多少列的 Feature
+    # 求第一行有多少列的 Feature, 最后一列是label列嘛
     numFeatures = len(dataSet[0]) - 1
     # label的信息熵
     baseEntropy = calcShannonEnt(dataSet)
@@ -147,8 +139,6 @@ def majorityCnt(classList):
         classList label列的集合
     Returns:
         bestFeature 最优的特征列
-    Raises:
-
     """
     classCount = {}
     for vote in classList:
@@ -172,6 +162,7 @@ def createTree(dataSet, labels):
 
     # 选择最优的列，得到最有列对应的label含义
     bestFeat = chooseBestFeatureToSplit(dataSet)
+    # 获取label的名称
     bestFeatLabel = labels[bestFeat]
     # 初始化myTree
     myTree = {bestFeatLabel: {}}
@@ -190,16 +181,26 @@ def createTree(dataSet, labels):
 
 
 def classify(inputTree, featLabels, testVec):
-    # 获取tree的第一个节点对应的key值
+    """classify(给输入的节点，进行分类)
+
+    Args:
+        inputTree  决策树模型
+        featLabels label标签对应的名称
+        testVec    测试输入的数据
+    Returns:
+        classLabel 分类的结果值，需要映射label才能知道名称
+    """
+    # 获取tree的根节点对于的key值
     firstStr = inputTree.keys()[0]
-    # 获取第一个节点对应的value值
+    # 通过key得到根节点对应的value
     secondDict = inputTree[firstStr]
-    # 判断根节点的索引值，然后根据testVec来获取对应的树分枝位置
+    # 判断根节点名称获取根节点在label中的先后顺序，这样就知道输入的testVec怎么开始对照树来做分类
     featIndex = featLabels.index(firstStr)
+    # 测试数据，找到根节点对应的label位置，也就知道从输入的数据的第几位来开始分类
     key = testVec[featIndex]
     valueOfFeat = secondDict[key]
     print '+++', firstStr, 'xxx', secondDict, '---', key, '>>>', valueOfFeat
-    # 判断分枝是否结束
+    # 判断分枝是否结束: 判断valueOfFeat是否是dict类型
     if isinstance(valueOfFeat, dict):
         classLabel = classify(valueOfFeat, featLabels, testVec)
     else:
@@ -240,7 +241,7 @@ if __name__ == "__main__":
     myTree = createTree(myDat, copy.deepcopy(labels))
     print myTree
     # [1, 1]表示要取的分支上的节点位置，对应的结果值
-    # print classify(myTree, labels, [1, 1])
+    print classify(myTree, labels, [1, 1])
 
     # 画图可视化展现
     dtPlot.createPlot(myTree)

src/python/03.DecisionTree/DecisionTreePlot.py

@@ -128,5 +128,5 @@ def retrieveTree(i):
     return listOfTrees[i]
 
 
-myTree = retrieveTree(1)
-createPlot(myTree)
+# myTree = retrieveTree(1)
+# createPlot(myTree)

src/python/07.AdaBoost/adaboost.py

@@ -201,7 +201,7 @@ def plotROC(predStrengths, classLabels):
             ySum += cur[1]
         # draw line from cur to (cur[0]-delX, cur[1]-delY)
         # 画点连线 (x1, x2, y1, y2)
-        print cur[0], cur[0]-delX, cur[1], cur[1]-delY
+        # print cur[0], cur[0]-delX, cur[1], cur[1]-delY
         ax.plot([cur[0], cur[0]-delX], [cur[1], cur[1]-delY], c='b')
         cur = (cur[0]-delX, cur[1]-delY)
     # 画对角的虚线线

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)