机器学习实战第三章，决策树的实现
新建tree.py模块，写入下列代码，这里的所有函数共同完成了建立一个决策树
from math import log
import numpy as np
import matplotlib as plt
import operator
def calcShannonEnt(dataSet):
# 计算给定数据的香农熵
numEntries = len(dataSet)
labelCounts = {}
for featVec in dataSet:
currentLabel = featVec[-1]
labelCounts[currentLabel] = labelCounts.get(currentLabel,0) + 1
shannonEnt = 0.0
for key in labelCounts:
prob = float(labelCounts[key])/numEntries
shannonEnt -= prob * log(prob, 2)
return shannonEnt
def createDataSet():
#产生一个样本集
dateSet = [[1,1,'yes'],
[1,1,'yes'],
[1,0,'no'],
[0,1,'no'],
[0,1,'no']]
labels = ['no surfacing', 'fippers']
return dateSet,labels
def splitDataSet(dataSet, axis, value):
#按照value的值划分数据集
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):
#选择最好的数据集划分方式，返回特征索引
numFeatures = len(dataSet[0]) - 1
baseEntropy = calcShannonEnt(dataSet)
bestInfoGain = 0.0; bestFeatures = -1
for i in range(numFeatures):
featList = [example[i] for example in dataSet]
uniqueVals = set(featList)
newEntropy = 0.0
for value in uniqueVals:
subDataSet = splitDataSet(dataSet,i,value)
prob = float(len(subDataSet))/float(len(dataSet))
newEntropy += prob*calcShannonEnt(subDataSet)
infogain = baseEntropy - newEntropy
if(infogain > bestInfoGain):
bestInfoGain = infogain
bestFeatures = i
return bestFeatures
def majorityCnt(classList):
#当用于划分的特征已经遍历完但是分类还是不唯一，这时就
#只能强制他们变成一个分类，选择出现频率最高的作为分类
classCount = {}
for vote in classList:
classCount[vote] = classCount.get(vote,0) + 1
sortedClassCount = sorted(classCount.items(), key=operator.getitem(1), reverse=True)
return sortedClassCount[0][0]
def createTree(dataSet,labels):
#构建决策树，字典表示树结构
classList = [example[-1] for example in dataSet]
if classList.count(classList[0]) == len(classList):
return classList[0]
if len(dataSet) == 1:
return majorityCnt(classList)
bestFeat = chooseBestFeatureToSplit(dataSet)
bestFeatLabel = labels[bestFeat]
mytree = {bestFeatLabel:{}}
subLabels = labels[:bestFeat]
subLabels.extend(labels[bestFeat+1:])
featValues = [example[bestFeat] for example in dataSet]
uniqueVales = set(featValues)
for value in uniqueVales:
subDataSet = splitDataSet(dataSet, bestFeat, value)
mytree[bestFeatLabel][value] = createTree(subDataSet,subLabels)
return mytree
def classfy(inputTree, featLabels,testVec):
'''
featLabel是各个特征的名字，testVec是特征向量，inputTree是决策树，返回分类
:param inputTree:
:param featLabels:
:param testVec:
:return:
'''
firstStr = list(inputTree.keys())[0]
secondDict = inputTree[firstStr]
featIndex = featLabels.index(firstStr)
for key in secondDict:
if key == testVec[featIndex]:
if type(secondDict[key]).__name__ == 'dict':
classLabel = classfy(secondDict[key], featLabels,testVec)
else: classLabel = secondDict[key]
return classLabel
def storeTree(inputTree, filename):
'''
序列化决策树,存入文件
:param inputTree:
:param filename:
:return:
'''
import pickle
fw = open(filename,'wb')
pickle.dump(inputTree,fw)
fw.close()
def grabTree(filename):
'''
将文件转换为决策树到内存
:param filename:
:return:
'''
import pickle
fr = open(filename,'r')
return pickle.load(fr)
createDataSet函数已经表明，这里样本集dataSet应该定义成元素是列表的列表，并且元素列表中的最后一列是类别，前面几列是特征
labels是对应的特征列的特征名。
下面是treePlot.py模块，用于绘制决策树
import matplotlib.pyplot as plt
decisionNode = dict(boxstyle='sawtooth', fc='10')
leafNode = dict(boxstyle='round4',fc='0.8')
arrow_args = dict(arrowstyle='<|-')
def plotNode(nodeTxt, centerPt, parentPt, nodeType):
createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction',\
xytext=centerPt,textcoords='axes fraction',\
va='center', ha='center',bbox=nodeType,arrowprops\
=arrow_args)
def getNumLeafs(myTree):
numLeafs = 0
firstStr = list(myTree.keys())[0]
secondDict = myTree[firstStr]
for key in secondDict:
if(type(secondDict[key]).__name__ == 'dict'):
numLeafs += getNumLeafs(secondDict[key])
else:
numLeafs += 1
return numLeafs
def getTreeDepth(myTree):
maxDepth = 0
firstStr = list(myTree.keys())[0]
secondDict = myTree[firstStr]
for key in secondDict:
if(type(secondDict[key]).__name__ == 'dict'):
thisDepth = 1+getTreeDepth((secondDict[key]))
else:
thisDepth = 1
if thisDepth > maxDepth: maxDepth = thisDepth
return maxDepth
def retrieveTree(i):
#预先设置树的信息
listOfTree = [{'no surfacing':{0:'no', 1:{'flipper':{0:'no', 1:'yes'}}}},
{'no surfacing':{0:'no', 1:{'flipper':{0:{'head':{0:'no', 1:'yes'}},1:'no'}}}},
{'a1':{0:'b1', 1:{'b2':{0:{'c1':{0:'d1',1:'d2'}}, 1:'c2'}}, 2:'b3'}}]
return listOfTree[i]
def createPlot(inTree):
fig = plt.figure(1,facecolor='white')
fig.clf()
axprops = dict(xticks = [0.2,0.4,0.6], yticks=[0.2,0.4,0.6])
createPlot.ax1 = plt.subplot(111)
plotTree.totalW = float(getNumLeafs(inTree))
plotTree.totalD = float(getTreeDepth(inTree))
plotTree.xOff = -0.5/plotTree.totalW;plotTree.yOff = 1.0
plotTree(inTree,(0.5,1.0), '')
plt.show()
def plotMidText(cntrPt, parentPt,txtString):
xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0]
yMid = (parentPt[1] - cntrPt[1])/2.0 + cntrPt[1]
createPlot.ax1.text(xMid, yMid, txtString)
def plotTree(myTree, parentPt = (0.5,1.0), nodeTxt = ""):
numLeafs = getNumLeafs(myTree)
depth = getTreeDepth(myTree)
firstStr = list(myTree.keys())[0]
cntrPt = (plotTree.xOff+(1.0+float(numLeafs))/2.0/plotTree.totalW,\
plotTree.yOff)
plotMidText(cntrPt,parentPt,nodeTxt)
plotNode(firstStr,cntrPt,parentPt,decisionNode)
secondDict = myTree[firstStr]
plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD
for key in secondDict:
if type(secondDict[key]).__name__ == 'dict':
plotTree(secondDict[key],cntrPt,str(key))
else:
plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW
plotNode(secondDict[key],(plotTree.xOff,plotTree.yOff),\
cntrPt,leafNode)
plotMidText((plotTree.xOff,plotTree.yOff),cntrPt,str(key))
plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD