决策树预测隐形眼镜的类型(python机器学习记录)

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使用决策树预测隐形眼镜类型

tree.py(创建树的代码)

# -*- coding: UTF-8 -*-
from math import log
import operator#计算数据集的香农熵     熵越高数据越乱
def calcShannonEnt(dataSet):numEntries = len(dataSet)labelCounts = {}for featVec in dataSet:currentLabel = featVec[-1]if currentLabel not in labelCounts.keys(): labelCounts[currentLabel] = 0labelCounts[currentLabel] += 1shannonEnt = 0.0for key in labelCounts:prob = float(labelCounts[key]) / numEntriesshannonEnt -= prob * log(prob, 2)return shannonEnt#划分数据集
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)# print retDataSetreturn retDataSet#选择最好的数据集划分方式
def chooseBestFeatureTosplit(dataSet):numFeatures=len(dataSet[0])-1baseEntropy=calcShannonEnt(dataSet)bestInfoGain=0.0;bestFeature=-1for i in range(numFeatures):featList=[example[i] for example in dataSet]uniqueVals=set(featList)newEntorpy=0.0for value in uniqueVals:subDataSet =splitDataSet(dataSet,i,value)prob=len(subDataSet)/float(len(dataSet))newEntorpy+=prob*calcShannonEnt(subDataSet)infoGain=baseEntropy-newEntorpyif(infoGain>bestInfoGain):bestInfoGain=infoGainbestFeature=ireturn  bestFeature
def majorityCnt(classList):classCount={}for vote in classList:if vote not in classCount.keys(): classCount[vote] = 0classCount[vote] += 1sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)return sortedClassCount[0][0]#创建树
def createTree(dataSet,labels):classList = [example[-1] for example in dataSet]if classList.count(classList[0]) == len(classList):return classList[0]#stop splitting when all of the classes are equalif len(dataSet[0]) == 1: #stop splitting when there are no more features in dataSetreturn 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[:]       #copy all of labels, so trees don't mess up existing labelsmyTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)return myTree

treePlotter.py(绘制树的代码)

# -*- coding: UTF-8 -*-
import sys
reload(sys)
sys.setdefaultencoding('gbk')import matplotlib.pyplot as pltdecisionNode = dict(boxstyle="sawtooth", fc="0.8")
leafNode = dict(boxstyle="round4", fc="0.8")
arrow_args = dict(arrowstyle="<-")#获取叶节点的数目
def getNumLeafs(myTree):numLeafs = 0firstStr = myTree.keys()[0]secondDict = myTree[firstStr]for key in secondDict.keys():if type(secondDict[key]).__name__ == 'dict':  # test to see if the nodes are dictonaires, if not they are leaf nodesnumLeafs += getNumLeafs(secondDict[key])else:numLeafs += 1return numLeafs#获取叶节点的层数
def getTreeDepth(myTree):maxDepth = 0firstStr = myTree.keys()[0]secondDict = myTree[firstStr]for key in secondDict.keys():if type(secondDict[key]).__name__ == 'dict':  # test to see if the nodes are dictonaires, if not they are leaf nodesthisDepth = 1 + getTreeDepth(secondDict[key])else:thisDepth = 1if thisDepth > maxDepth: maxDepth = thisDepthreturn maxDepthdef 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 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):  # if the first key tells you what feat was split onnumLeafs = getNumLeafs(myTree)  # this determines the x width of this treedepth = getTreeDepth(myTree)firstStr = myTree.keys()[0]  # the text label for this node should be thiscntrPt = (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.totalDfor key in secondDict.keys():if type(secondDict[key]).__name__ == 'dict':  # test to see if the nodes are dictonaires, if not they are leaf nodesplotTree(secondDict[key], cntrPt, str(key))  # recursionelse:  # it's a leaf node print the leaf nodeplotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalWplotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))plotTree.yOff = plotTree.yOff + 1.0 / plotTree.totalD#主函数
def createPlot(inTree):fig = plt.figure(1, facecolor='white')fig.clf()axprops = dict(xticks=[], yticks=[])createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)  # no ticks# createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropsesplotTree.totalW = float(getNumLeafs(inTree))plotTree.totalD = float(getTreeDepth(inTree))plotTree.xOff = -0.5 / plotTree.totalWplotTree.yOff = 1.0plotTree(inTree, (0.5, 1.0), '')plt.show()

1. 收集数据：lenses.txt

   young	myope	no	reduced	no lenses
   young	myope	no	normal	soft
   young	myope	yes	reduced	no lenses
   young	myope	yes	normal	hard
   young	hyper	no	reduced	no lenses
   young	hyper	no	normal	soft
   young	hyper	yes	reduced	no lenses
   young	hyper	yes	normal	hard
   pre	myope	no	reduced	no lenses
   pre	myope	no	normal	soft
   pre	myope	yes	reduced	no lenses
   pre	myope	yes	normal	hard
   pre	hyper	no	reduced	no lenses
   pre	hyper	no	normal	soft
   pre	hyper	yes	reduced	no lenses
   pre	hyper	yes	normal	no lenses
   presbyopic	myope	no	reduced	no lenses
   presbyopic	myope	no	normal	no lenses
   presbyopic	myope	yes	reduced	no lenses
   presbyopic	myope	yes	normal	hard
   presbyopic	hyper	no	reduced	no lenses
   presbyopic	hyper	no	normal	soft
   presbyopic	hyper	yes	reduced	no lenses
   presbyopic	hyper	yes	normal	no lenses
   

2. 准备数据：解析tab键分隔符的
3. 分析数据：快速检测数据，确保正确的解析数据内容，使用createPlot()函数绘制最终的树形图。
4. 训练算法：使用createTree()函数
5. 测试算法：编写测试函数验证决策树可以正确分类给定的数据实例
6. 使用算法：存储数的数据结构，以便下次使用时无需重新构造树

在导入tree.py和treePlotter.py的python交互模式下：

>>>import tree
>>>import treePlotter
>>>fr=open("lenses.txt")
>>>lenses=[inst.strip().split("\t") for inst in fr.readlines()]
>>>lensesLabels=["age","prescript","astigmatic","tearRte"]
>>>lensesTree=tree.createTree(lenses,lensesLabels)
>>>treePlotter.createPlot(lensesTree)

绘制的决策树：

参考书籍：机器学习实战

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