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1、精選優(yōu)質(zhì)文檔-傾情為你奉上武漢理工大學(xué)理學(xué)院數(shù)學(xué)系課程上機實驗報告課 程 名 稱： 數(shù)據(jù)挖掘 班級信計1201日 期6.9成績評定 姓名張徐軍（26）李雪梅（35）張曉婷（33）實驗室數(shù)學(xué)207老師簽名  實驗名稱決策樹解決肝癌預(yù)測問題所用軟件 Python實驗?zāi)康募皟?nèi)容目的 ：熟悉決策樹分類的基本思想以及運用ID3算法進行實例演練。內(nèi)容：根據(jù)所給的肝癌數(shù)據(jù)進行分類，得到?jīng)Q策樹，以及驗證其正確性與適用性。實驗 原理步驟在編碼之前為了方便起見，將實驗數(shù)據(jù)的十個指標(biāo)的屬性值分別用數(shù)字1，2，3，4表示，表示結(jié)果如下：X1 no 1 light 2 mid 3

2、 serious 4X2 no 1 branch 2 trunk 3X3 positive 1 negative 2X4 positive 1 negative 2X5 rightliver 1 leftliver 2 allliver 3X6 small 1 middle 2 big 3 verybig 4X7 dilation 1 infiltration 2X8 no 1 part 2 integrate 3X9 no 1 have 2X10 no 1 less 2 much 3代碼：# -*- coding: cp936 -*-import mathimport operator#計算

3、香農(nóng)熵，分兩步，第一步計算頻率，第二步根據(jù)公式計算香農(nóng)熵。def calcShannonEnt(dataSet): numEntries=len(dataSet) labelCounts= for featVec in dataSet: currentLabel=featVec-1 if currentLabel not in labelCounts.keys(): labelCountscurrentLabel=0 labelCountscurrentLabel+=1 shannonEnt=0.0 for key in labelCounts: prob =float(labelCounts

4、key)/numEntries shannonEnt-=prob*math.log(prob,2) return shannonEnt def createDataSet(): dataSet=3,2,2,2,1,2,1,2,1,2,'Y', 3,3,1,1,1,2,2,1,2,3,'N', 4,1,2,1,2,3,1,1,1,3,'Y', 1,1,2,2,3,4,1,3,1,3,'Y', 2,2,1,1,1,1,2,3,2,1,'N', 3,3,1,2,1,2,2,2,1,1,'Y', 2,2,1

5、,2,1,1,2,1,2,3,'Y', 1,3,2,1,3,3,1,2,1,2,'N', 3,2,1,2,1,2,1,3,2,2,'N', 1,1,2,1,1,4,1,2,1,1,'N', 4,3,2,2,1,3,2,3,2,2,'N', 2,3,1,2,3,1,1,1,1,2,'Y', 1,1,2,1,1,4,2,2,1,3,'N', 1,2,2,2,3,4,2,3,2,1,'N', 4,2,1,1,1,3,2,2,2,2,'Y', 3,1,2,1,

6、1,2,1,3,2,3,'N', 3,2,2,2,1,2,1,3,1,2,'N', 2,3,2,1,2,1,2,1,1,1,'Y', 1,3,2,1,1,4,2,1,1,1,'N', 1,1,1,1,1,4,1,2,1,2,'Y' labels='X1','X2','X3','X4','X5','X6','X7','X8','X9','X10' return

7、 dataSet,labels#劃分?jǐn)?shù)據(jù)集，將滿足Xaixs=value的值都劃分到一起，返回一個劃分好的集合（不包括用來劃分的aixs屬性，因為不需要）def splitDataSet(dataSet, axis, value): retDataSet = for featVec in dataSet: if featVecaxis = value: reducedFeatVec = featVec:axis #chop out axis used for splitting reducedFeatVec.extend(featVecaxis+1:) retDataSet.append(re

8、ducedFeatVec) return retDataSet#選擇最好的屬性進行劃分，思路很簡單就是對每個屬性都劃分下，看哪個好。這里使用到了一個set來選取列表中唯一的元素。def chooseBestFeatureToSplit(dataSet): numFeatures = len(dataSet0) - 1 #因為數(shù)據(jù)集的最后一項是標(biāo)簽 baseEntropy = calcShannonEnt(dataSet) bestInfoGain = 0.0; bestFeature = -1 for i in range(numFeatures): #iterate over all the

9、 features featList = examplei for example in dataSet#create a list of all the examples of this feature uniqueVals = set(featList) #get a set of unique values newEntropy = 0.0 for value in uniqueVals: subDataSet = splitDataSet(dataSet, i, value) prob = len(subDataSet)/float(len(dataSet) newEntropy +=

10、 prob * calcShannonEnt(subDataSet) infoGain = baseEntropy - newEntropy #calculate the info gain; ie reduction in entropy if (infoGain > bestInfoGain): #compare this to the best gain so far bestInfoGain = infoGain #if better than current best, set to best bestFeature = i return bestFeature #return

11、s an integer #因為我們遞歸構(gòu)建決策樹是根據(jù)屬性的消耗進行計算的，所以可能會存在最后屬性用完了，但是分類還是沒有完，這是就會采用多數(shù)表決的方式計算節(jié)點分類。 def majorityCnt(classList): classCount= for vote in classList: if vote not in classCount.keys(): classCountvote = 0 classCountvote += 1 sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), r

12、everse=True) return sortedClassCount00#基于遞歸構(gòu)建決策樹。這里的label更多是對于分類特征的名字，為了更好理解。def createTree(dataSet,labels): classList = example-1 for example in dataSet if classList.count(classList0) = len(classList): return classList0#stop splitting when all of the classes are equal if len(dataSet0) = 1: #stop sp

13、litting when there are no more features in dataSet return majorityCnt(classList) bestFeat = chooseBestFeatureToSplit(dataSet) bestFeatLabel = labelsbestFeat myTree = bestFeatLabel: del(labelsbestFeat) featValues = examplebestFeat for example in dataSet uniqueVals = set(featValues) for value in uniqu

14、eVals: subLabels = labels: #copy all of labels, so trees don't mess up existing labels myTreebestFeatLabelvalue = createTree(splitDataSet(dataSet, bestFeat, value),subLabels) return myTreedef classify(inputTree,featLabels,testVec): firstStr = inputTree.keys()0 secondDict = inputTreefirstStr feat

15、Index = featLabels.index(firstStr) key = testVecfeatIndex valueOfFeat = secondDictkey if isinstance(valueOfFeat, dict): classLabel = classify(valueOfFeat, featLabels, testVec) else: classLabel = valueOfFeat return classLabeldef getResult(): dataSet,labels=createDataSet() # splitDataSet(dataSet,1,1)

16、chooseBestFeatureToSplit(dataSet) # print chooseBestFeatureToSplit(dataSet) #print calcShannonEnt(dataSet) mtree=createTree(dataSet,labels) print mtree print classify(mtree,'X1','X2','X3','X4','X5','X6','X7','X8','X9','X10&#

17、39;, 3,1,2,1,1,2,1,3,2,3)print classify(mtree,'X1','X2','X3','X4','X5','X6','X7','X8','X9','X10',3,2,2,2,1,2,1,3,1,2)print classify(mtree,'X1','X2','X3','X4','X5','X6','

18、;X7','X8','X9','X10',2,3,2,1,2,1,2,1,1,1)print classify(mtree,'X1','X2','X3','X4','X5','X6','X7','X8','X9','X10',1,3,2,1,1,4,2,1,1,1) print classify(mtree,'X1','X2','X3'

19、;,'X4','X5','X6','X7','X8','X9','X10', 1,1,1,1,1,4,1,2,1,2) if _name_='_main_': getResult() 實驗結(jié)果及分析第一步：首先將20個樣本作為訓(xùn)練樣本生成決策樹，然后以原來的20個樣本作為測試樣本檢驗決策樹的正確性及適用性，得到的結(jié)果與事實全部相符，決策樹程序表示如下：'X8': 1: 'X1': 1: 'N', 2: 'Y', 3: 'N', 4: 'Y', 2: 'X1': 1: 'X3': 1: 'Y', 2: 'N', 3: 'Y', 4: 'Y', 3: 'X1': 1: 'X2': 1: 'Y', 2: 'N', 2: &