决策树算法的应用python实现_python怎么画出决策树的分支

from math import log
import operator
def calcShannonEnt(dataSet):  # 计算数据的熵(entropy)
numEntries=len(dataSet)  # 数据条数
labelCounts={}
for featVec in dataSet:
currentLabel=featVec[-1] # 每行数据的最后一个字（类别）
if currentLabel not in labelCounts.keys():
labelCounts[currentLabel]=0
labelCounts[currentLabel]+=1  # 统计有多少个类以及每个类的数量
shannonEnt=0
for key in labelCounts:
prob=float(labelCounts[key])/numEntries # 计算单个类的熵值
shannonEnt-=prob*log(prob,2) # 累加每个类的熵值
return shannonEnt
def createDataSet1():    # 创造示例数据
dataSet = [['长', '粗', '男'],
['短', '粗', '男'],
['短', '粗', '男'],
['长', '细', '女'],
['短', '细', '女'],
['短', '粗', '女'],
['长', '粗', '女'],
['长', '粗', '女']]
labels = ['头发','声音']  #两个特征
return dataSet,labels
def splitDataSet(dataSet,axis,value): # 按某个特征分类后的数据
retDataSet=[]
for featVec in dataSet:
if featVec[axis]==value:
reducedFeatVec =featVec[:axis]
reducedFeatVec.extend(featVec[axis+1:])
retDataSet.append(reducedFeatVec)
return retDataSet
def chooseBestFeatureToSplit(dataSet):  # 选择最优的分类特征
numFeatures = len(dataSet[0])-1
baseEntropy = calcShannonEnt(dataSet)  # 原始的熵
bestInfoGain = 0
bestFeature = -1
for i in range(numFeatures):
featList = [example[i] for example in dataSet]
uniqueVals = set(featList)
newEntropy = 0
for value in uniqueVals:
subDataSet = splitDataSet(dataSet,i,value)
prob =len(subDataSet)/float(len(dataSet))
newEntropy +=prob*calcShannonEnt(subDataSet)  # 按特征分类后的熵
infoGain = baseEntropy - newEntropy  # 原始熵与按特征分类后的熵的差值
if (infoGain>bestInfoGain):   # 若按某特征划分后，熵值减少的最大，则次特征为最优分类特征
bestInfoGain=infoGain
bestFeature = i
return bestFeature
def majorityCnt(classList):    #按分类后类别数量排序，比如：最后分类为2男1女，则判定为男；
classCount={}
for vote in classList:
if vote not in classCount.keys():
classCount[vote]=0
classCount[vote]+=1
sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]
def createTree(dataSet,labels):
classList=[example[-1] for example in dataSet]  # 类别：男或女
if classList.count(classList[0])==len(classList):
return classList[0]
if len(dataSet[0])==1:
return majorityCnt(classList)
bestFeat=chooseBestFeatureToSplit(dataSet) #选择最优特征
bestFeatLabel=labels[bestFeat]
myTree={bestFeatLabel:{}} #分类结果以字典形式保存
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
if __name__=='__main__':
dataSet, labels=createDataSet1()  # 创造示列数据
print(createTree(dataSet, labels))  # 输出决策树模型结果

{'声音': {'细': '女', '粗': {'头发': {'短': '男', '长': '女'}}}}