数据挖掘习题之决策树算法python,数据挖掘前端~米老鼠的博客-

根据processed.cleveland.data数据进行数据挖掘操作
UCI公开数据集-heartdisease，属性信息如下：
1.#3(age)
2.#4(sex)
3.#9(cp)
4.#10(trestbps)
5.#12(chol)
6.#16(fbs)
7.#19(restecg)
8.#32(thalach)
9.#38(exang)
10.#40(oldpeak)
11.#41(slope)
12.#44(ca)
13.#51(thal)
14.#58(num)(thepredictedattribute)
数据集参考网址：https://archive.ics.uci.edu/ml/datasets/Heart+Disease

import math  import operator   def calcShannonEnt(dataset):     numEntries = len(dataset)     labelCounts = {} for featVec in dataset:         currentLabel = featVec[-1] if currentLabel not in labelCounts.keys():             labelCounts[currentLabel] = 0         labelCounts[currentLabel] +=1               shannonEnt = 0.0 for key in labelCounts:         prob = float(labelCounts[key])/numEntries         shannonEnt -= prob*math.log(prob, 2) return shannonEnt       def CreateDataSet(): #字符串转化浮点数 def safe_float(number): try: return float(number) except: return None #读取数据     dataset=[] with open('processed.cleveland.data') as read_file: for line in read_file:             line=line.replace('n','').split(',')             line=list(map(safe_float,line))             dataset.append(line)     labels = ['age','sex','cp','trestbps','chol','fbs','restecg','thalach','thalach','exang','oldpeak','slope','ca','thal','num'] return dataset, labels   def splitDataSet(dataSet, axis, value):     retDataSet = [] for featVec in dataSet: if featVec[axis] == value:             reducedFeatVec = featVec[:axis]             reducedFeatVec.extend(featVec[axis+1:])             retDataSet.append(reducedFeatVec) return retDataSet def majorityCnt(classList):     classCount ={} for vote in classList: if vote not in classCount.keys():             classCount[vote]=0         classCount[vote]=1     sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) return sortedClassCount[0][0] def chooseBestFeatureToSplit(dataSet):     numberFeatures = len(dataSet[0])-1     baseEntropy = calcShannonEnt(dataSet)     bestInfoGain = 0.0;     bestFeature = -1; for i in range(numberFeatures):         featList = [example[i] for example in dataSet]         uniqueVals = set(featList)         newEntropy =0.0 for value in uniqueVals:             subDataSet = splitDataSet(dataSet, i, value)             prob = len(subDataSet)/float(len(dataSet))             newEntropy += prob * calcShannonEnt(subDataSet)         infoGain = baseEntropy - newEntropy         if(infoGain > bestInfoGain):             bestInfoGain = infoGain             bestFeature = i     return bestFeature      def createTree(dataSet, labels):     classList = [example[-1] for example in dataSet] if classList.count(classList[0])==len(classList): return classList[0] if len(dataSet[0])==1: return majorityCnt(classList)     bestFeat = chooseBestFeatureToSplit(dataSet)     bestFeatLabel = labels[bestFeat]     myTree = {bestFeatLabel:{}} del(labels[bestFeat])     featValues = [example[bestFeat] for example in dataSet]     uniqueVals = set(featValues) for value in uniqueVals:         subLabels = labels[:]         myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels) return myTree                       MyData,label = CreateDataSet() #决策树ID3 createTree(MyData,label) 

运行结果：