数据挖掘实验报告

1、精选优质文档-倾情为你奉上武汉理工大学理学院数学系课程上机实验报告课 程 名 称： 数据挖掘 班级信计1201日 期6.9成绩评定 姓名张徐军（26）李雪梅（35）张晓婷（33）实验室数学207老师签名  实验名称决策树解决肝癌预测问题所用软件 Python实验目的及内容目的 ：熟悉决策树分类的基本思想以及运用ID3算法进行实例演练。内容：根据所给的肝癌数据进行分类，得到决策树，以及验证其正确性与适用性。实验 原理步骤在编码之前为了方便起见，将实验数据的十个指标的属性值分别用数字1，2，3，4表示，表示结果如下：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代码：# -*- coding: cp936 -*-import mathimport operator#计算

3、香农熵，分两步，第一步计算频率，第二步根据公式计算香农熵。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#划分数据集，将满足Xaixs=value的值都划分到一起，返回一个划分好的集合（不包括用来划分的aixs属性，因为不需要）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#选择最好的属性进行划分，思路很简单就是对每个属性都划分下，看哪个好。这里使用到了一个set来选取列表中唯一的元素。def chooseBestFeatureToSplit(dataSet): numFeatures = len(dataSet0) - 1 #因为数据集的最后一项是标签 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 #因为我们递归构建决策树是根据属性的消耗进行计算的，所以可能会存在最后属性用完了，但是分类还是没有完，这是就会采用多数表决的方式计算节点分类。 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#基于递归构建决策树。这里的label更多是对于分类特征的名字，为了更好理解。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() 实验结果及分析第一步：首先将20个样本作为训练样本生成决策树，然后以原来的20个样本作为测试样本检验决策树的正确性及适用性，得到的结果与事实全部相符，决策树程序表示如下：'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: &