# -*- coding: UTF-8 -*-
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
from math import log
import operator
def createDataSet():
dataSet = [[0, 0, 0, 0, 'no'],
[0, 0, 0, 1, 'no'],
[0, 1, 0, 1, 'yes'],
[0, 1, 1, 0, 'yes'],
[0, 0, 0, 0, 'no'],
[1, 0, 0, 0, 'no'],
[1, 0, 0, 1, 'no'],
[1, 1, 1, 1, 'yes'],
[1, 0, 1, 2, 'yes'],
[1, 0, 1, 2, 'yes'],
[2, 0, 1, 2, 'yes'],
[2, 0, 1, 1, 'yes'],
[2, 1, 0, 1, 'yes'],
[2, 1, 0, 2, 'yes'],
[2, 0, 0, 0, 'no']]
labels = ['F1-AGE', 'F2-WORK', 'F3-HOME', 'F4-LOAN']
return dataSet, labels
def createTree(dataset,labels,featLabels):
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]
featLabels.append(bestFeatLabel)
myTree = {bestFeatLabel:{}}
del labels[bestFeat]
featValue = [example[bestFeat] for example in dataset]
uniqueVals = set(featValue)
for value in uniqueVals:
sublabels = labels[:]
myTree[bestFeatLabel][value] = createTree(splitDataSet(dataset,bestFeat,value),sublabels,featLabels)
return myTree
def majorityCnt(classList):
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 chooseBestFeatureToSplit(dataset):
numFeatures = len(dataset[0]) - 1
baseEntropy = calcShannonEnt(dataset)
bestInfoGain = 0
bestFeature = -1
for i in range(numFeatures):
featList = [example[i] for example in dataset]
uniqueVals = set(featList)
newEntropy = 0
for val in uniqueVals:
subDataSet = splitDataSet(dataset,i,val)
prob = len(subDataSet)/float(len(dataset))
newEntropy += prob * calcShannonEnt(subDataSet)
infoGain = baseEntropy - newEntropy
if (infoGain > bestInfoGain):
bestInfoGain = infoGain
bestFeature = i
return bestFeature
def splitDataSet(dataset,axis,val):
retDataSet = []
for featVec in dataset:
if featVec[axis] == val:
reducedFeatVec = featVec[:axis]
reducedFeatVec.extend(featVec[axis+1:])
retDataSet.append(reducedFeatVec)
return retDataSet
def calcShannonEnt(dataset):
numexamples = len(dataset)
labelCounts = {}
for featVec in dataset:
currentlabel = featVec[-1]
if currentlabel not in labelCounts.keys():
labelCounts[currentlabel] = 0
labelCounts[currentlabel] += 1
shannonEnt = 0
for key in labelCounts:
prop = float(labelCounts[key])/numexamples
shannonEnt -= prop*log(prop,2)
return shannonEnt
def getNumLeafs(myTree):
numLeafs = 0
firstStr = next(iter(myTree))
secondDict = myTree[firstStr]
for key in secondDict.keys():
if type(secondDict[key]).__name__=='dict':
numLeafs += getNumLeafs(secondDict[key])
else: numLeafs +=1
return numLeafs
def getTreeDepth(myTree):
maxDepth = 0
firstStr = next(iter(myTree))
secondDict = myTree[firstStr]
for key in secondDict.keys():
if type(secondDict[key]).__name__=='dict':
thisDepth = 1 + getTreeDepth(secondDict[key])
else: thisDepth = 1
if thisDepth > maxDepth: maxDepth = thisDepth
return maxDepth
def plotNode(nodeTxt, centerPt, parentPt, nodeType):
arrow_args = dict(arrowstyle="<-")
font = FontProperties(fname=r"c:\windows\fonts\simsunb.ttf", size=14)
createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction',
xytext=centerPt, textcoords='axes fraction',
va="center", ha="center", bbox=nodeType, arrowprops=arrow_args, FontProperties=font)
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, va="center", ha="center", rotation=30)
def plotTree(myTree, parentPt, nodeTxt):
decisionNode = dict(boxstyle="sawtooth", fc="0.8")
leafNode = dict(boxstyle="round4", fc="0.8")
numLeafs = getNumLeafs(myTree)
depth = getTreeDepth(myTree)
firstStr = next(iter(myTree))
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.keys():
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
def createPlot(inTree):
fig = plt.figure(1, facecolor='white') #创建fig
fig.clf() #清空fig
axprops = dict(xticks=[], yticks=[])
createPlot.ax1 = plt.subplot(111, frameon=False, **axprops) #去掉x、y轴
plotTree.totalW = float(getNumLeafs(inTree)) #获取决策树叶结点数目
plotTree.totalD = float(getTreeDepth(inTree)) #获取决策树层数
plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0; #x偏移
plotTree(inTree, (0.5,1.0), '') #绘制决策树
plt.show()
if __name__ == '__main__':
dataset, labels = createDataSet()
featLabels = []
myTree = createTree(dataset,labels,featLabels)
createPlot(myTree)
