【读书笔记】机器学习实战-第三章 决策树

机器学习实战

#!/usr/bin/python
# -*- coding: utf-8 -*-
from math import log
import operator
import treePlotter

def calcShannonEnt(dataSet):  # 计算集合熵
numEntires = len(dataSet)
labelCounts = {}
for featVec in dataSet:  # 统计频率
currentLabel = featVec[-1]
labelCounts[currentLabel] = labelCounts.get(currentLabel,0)+1  # dic.get()函数，访问不存在的键时，自动插入并设为默认值
shannonEnt = 0.0
for key in labelCounts.keys():  # 遍历字典，计算熵
prob = float(labelCounts[key])/numEntires
shannonEnt -= prob * log(prob, 2)  # log函数
return shannonEnt

def createDataSet():  # 测试数据
dataSet = [[1,1,'yes'],
[1,1,'yes'],
[1,0,'no'],
[0,1,'no'],
[0,1,'no']]
labels = ['no surfacing','flippers']  # flippers: 脚蹼
return dataSet, labels

def splitDataSet(dataSet, axis, value):    # 返回第axis特征值为value的列表
retDataSet = []                         # 新对象，防止传入参数被修改
for featVec in dataSet:                 # 寻找符合要求的值，添加到新列表中
if featVec[axis] == value:
reduceFeatVec = featVec[:axis]  # 除去第axis特征
reduceFeatVec.extend(featVec[axis+1:])  # extend ：在列表末尾一次性添加另一个序列的多个值
retDataSet.append(reduceFeatVec)        # 列表末尾添加源元素
return retDataSet

def chooseBestFeatureToSplit(dataSet):
numFeatures
f4d3
= len(dataSet[0])-1         # 特征个数
baseEntropy = calcShannonEnt(dataSet)   # 计算原始集合熵
bestInfoGain = 0.0 ;bestFeature = -1;   # 最优解记录
for i in range(numFeatures):
featList = [example[i] for example in dataSet]  # 列表推导式
uniqueVals = set(featList)                       # 获取特征值取值范围
newEntropy = 0.0
for value in uniqueVals:
subDataSet = splitDataSet(dataSet, i, value)   # 获取被划分集合
prob = len(subDataSet)/float(len(dataSet))     # 计算划分集合所占比例
newEntropy += prob*calcShannonEnt(subDataSet)  # 计算熵
infoGain = baseEntropy - newEntropy                # 计算信息增益
if ( infoGain>bestInfoGain ):
bestInfoGain = infoGain
bestFeature = i
return bestFeature

def majoryCnt(classList):  # 终止块的分类：多数表决 输入：类列表
classCount = {}
for vote in classList:
classCount[vote] =classCount.get(vote, 0) + 1
sortedClassCount = sorted(classCount.iteritems(), key = operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]

def createTree(dataSet, labels):
classList = [example[-1] for example in dataSet]         # 列表推导式：获取包含的所有类别
if classList.count(classList[0]) == len(classList):      # [].count()：统计函数
return classList[0]                                 # 叶子节点
if len(dataSet[0]) == 1:
return majoryCnt(classList)                         # 终止块
bestFeat = chooseBestFeatureToSplit(dataSet)    # 确定分类特征
bestFeatLabel = labels[bestFeat]
myTree = {bestFeatLabel:{}}                     # 字典记录分类特征
del(labels[bestFeat])                           # 从特征列表删除已分类特征
featValues = [example[bestFeat] for example in dataSet]
uniqueVals = set(featValues)                    # 获取特征所有取值范围
for value in uniqueVals:
subLabels = labels[:]
myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet,bestFeat,value),subLabels)  # 递归子树
return myTree

def classify(inputTree,featLabels,testVec):  # 使用决策树决策
firstStr = inputTree.keys()[0]
secondDict = inputTree[firstStr]
featIndex = featLabels.index(firstStr)  # index: 获取当前特征的编号
key = testVec[featIndex]
valueOfFeat = secondDict[key]
if isinstance(valueOfFeat, dict):
classLabel = classify(valueOfFeat, featLabels, testVec)  # 递归
else: classLabel = valueOfFeat
return classLabel

def storeTree(inputTree, filename):  # 序列化对象
import pickle
fw = open(filename, 'w')
pickle.dump(inputTree, fw)
fw.close()

def grabTree(filename):
import pickle
fr = open(filename)
return pickle.load(fr)

fr = open('lenses.txt')
lenses = [sample.strip().split('\t') for sample in fr.readlines()]  # 列表推导式
lensesLabels = ['age','prescript','astigmatic','tearRate']           # 特征
lensesTree = createTree(lenses,lensesLabels)                         # 建树
treePlotter.createPlot(lensesTree)                                   # 作图

#myDat, labels = createDataSet()
#tmp = createTree(myDat,labels)

#tmp = chooseBestFeatureToSplit(myDat)
#splitDataSet(myDat,0,0)

#tmp = calcShannonEnt(myDat)
#pass

#!/usr/bin/python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt

# 定义文本框和箭头格式
decisionNode = dict(boxstyle="sawtooth", fc="0.8")
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 createPlot():
fig = plt.figure(1, facecolor= 'white')
fig.clf()
createPlot.ax1 = plt.subplot(111,frameon=False)  # 函数属性 可以在函数里面定义也可以在函数定义后加入也可以
plotNode('decision node',(0.5,0.1),(0.1,0.5),decisionNode)
plotNode('leaf node',(0.8,0.1),(0.3,0.8),leafNode)
plt.show()

def getNumLeafs(myTree):
numLeafs = 0
firstStr = 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 nodes
numLeafs += getNumLeafs(secondDict[key])
else:   numLeafs +=1
return numLeafs

def getTreeDepth(myTree):
maxDepth = 0
firstStr = 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 nodes
thisDepth = 1 + getTreeDepth(secondDict[key])
else:   thisDepth = 1
if thisDepth > maxDepth: maxDepth = thisDepth
return maxDepth

def retrieveTree(i):
listOfTrees =[{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},
{'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}
]
return listOfTrees[i]

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 on
numLeafs = getNumLeafs(myTree)  #this determines the x width of this tree
depth = getTreeDepth(myTree)
firstStr = myTree.keys()[0]     #the text label for this node should be this
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':#test to see if the nodes are dictonaires, if not they are leaf nodes
plotTree(secondDict[key],cntrPt,str(key))        #recursion
else:   #it's a leaf node print the leaf node
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')
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 puropses
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()

#createPlot()