《机器学习实战》个人学习记录笔记（五）———决策树构建与分类
第三章 决策树
PS：个人笔记 根据《机器学习实战》这本书，Jack-Cui的博客，以及深度眸的视频进行学习
1 决策树构建
ID3算法的核心是在决策树各个结点上对应信息增益准则选择特征，递归地构建决策树。具体方法是：从根结点(root node)开始，对结点计算所有可能的特征的信息增益，选择信息增益最大的特征作为结点的特征，由该特征的不同取值建立子节点；再对子结点递归地调用以上方法，构建决策树；直到所有特征的信息增益均很小或没有特征可以选择为止，最后得到一个决策树。ID3相当于用极大似然法进行概率模型的选择。
2 编写代码构建决策树
from math import log
import operator
def calcShannonEnt(dataSet):
numEntires = len(dataSet)
labelCounts = {}
for featVec in dataSet:
currentLabel = featVec[-1]
if currentLabel not in labelCounts.keys():
labelCounts[currentLabel] = 0
labelCounts[currentLabel] += 1
shannonEnt = 0.0
for key in labelCounts:
prob = float(labelCounts[key]) / numEntires
shannonEnt -= prob * log(prob, 2)
return shannonEnt
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 = ['年龄', '有工作', '有自己的房子', '信贷情况']
return dataSet, labels
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)
return retDataSet
def chooseBestFeatureToSplit(dataSet):
numFeatures = len(dataSet[0]) - 1
baseEntropy = calcShannonEnt(dataSet)
bestInfoGain = 0.0
bestFeature = -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 = len(subDataSet) / float(len(dataSet))
newEntropy += prob * calcShannonEnt(subDataSet)
infoGain = baseEntropy - newEntropy
# print("第%d个特征的增益为%.3f" % (i, infoGain))
if (infoGain > bestInfoGain):
bestInfoGain = infoGain
bestFeature = i
return bestFeature
"""
函数说明:统计classList中出现此处最多的元素(类标签)
Parameters:
classList - 类标签列表
Returns:
sortedClassCount[0][0] - 出现此处最多的元素(类标签)
"""
def majorityCnt(classList):
classCount = {}
for vote in classList: #统计classList中每个元素出现的次数
if vote not in classCount.keys():classCount[vote] = 0
classCount[vote] += 1
sortedClassCount = sorted(classCount.items(), key = operator.itemgetter(1), reverse = True) #根据字典的值降序排序
return sortedClassCount[0][0] #返回classList中出现次数最多的元素
"""
函数说明:创建决策树
Parameters:
dataSet - 训练数据集
labels - 分类属性标签
featLabels - 存储选择的最优特征标签
Returns:
myTree - 决策树
Author:
Jack Cui
Blog:
http://blog.csdn.net/c406495762
Modify:
2017-07-25
"""
def createTree(dataSet, labels, featLabels):
classList = [example[-1] for example in dataSet] #取分类标签(是否放贷:yes or no)
if classList.count(classList[0]) == len(classList): #如果类别完全相同则停止继续划分；count()计算一个类别的个数=类别列表里类别数
return classList[0]
if len(dataSet[0]) == 1: #遍历完所有特征时返回出现次数最多的类标签；没有特征时，用类别投票表决处理
return majorityCnt(classList)
bestFeat = chooseBestFeatureToSplit(dataSet) #选择最优特征 ；
bestFeatLabel = labels[bestFeat] #最优特征的标签；第一个是房子
featLabels.append(bestFeatLabel)
myTree = {bestFeatLabel:{}} #根据最优特征的标签生成树；{'有自己的房子':{}}
del(labels[bestFeat]) #删除已经使用特征标签 ；把房子那一列特征删除
featValues = [example[bestFeat] for example in dataSet] #得到训练集中所有最优特征的属性值；
uniqueVals = set(featValues) #去掉重复的属性值
for value in uniqueVals: #遍历特征，创建决策树。
myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), labels, featLabels) ⭐#假设第二列是最优特征，使用该特征作为根节点，进行递归，则原来的dataSet，会变成两个子dataSet,然后对这两个子dataSet分别进行递归创建树，直到满足结束条件。
return myTree
if __name__ == '__main__':
dataSet, labels = createDataSet()
featLabels = []
myTree = createTree(dataSet, labels, featLabels)
print(myTree)
3 决策树可视化（回头补）
4 决策树分类
from math import log
import operator
def calcShannonEnt(dataSet):
numEntires = len(dataSet)
labelCounts = {}
for featVec in dataSet:
currentLabel = featVec[-1]
if currentLabel not in labelCounts.keys():
labelCounts[currentLabel] = 0
labelCounts[currentLabel] += 1
shannonEnt = 0.0
for key in labelCounts:
prob = float(labelCounts[key]) / numEntires
shannonEnt -= prob * log(prob, 2)
return shannonEnt
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 = ['年龄', '有工作', '有自己的房子', '信贷情况']
return dataSet, labels
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)
return retDataSet
def chooseBestFeatureToSplit(dataSet):
numFeatures = len(dataSet[0]) - 1
baseEntropy = calcShannonEnt(dataSet)
bestInfoGain = 0.0
bestFeature = -1
for i in range(numFeatures):
#获取dataSet的第i个所有特征
featList = [example[i] for example in dataSet]
uniqueVals = set(featList)
newEntropy = 0.0
for value in uniqueVals:
subDataSet = splitDataSet(dataSet, i, value)
prob = len(subDataSet) / float(len(dataSet))
newEntropy += prob * calcShannonEnt(subDataSet)
infoGain = baseEntropy - newEntropy
# print("第%d个特征的增益为%.3f" % (i, infoGain))
if (infoGain > bestInfoGain):
bestInfoGain = infoGain
bestFeature = i
return bestFeature
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 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])
featValues = [example[bestFeat] for example in dataSet]
uniqueVals = set(featValues)
for value in uniqueVals:
myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), labels, featLabels)
return myTree
"""
函数说明:使用决策树分类
Parameters:
inputTree - 已经生成的决策树
featLabels - 存储选择的最优特征标签
testVec - 测试数据列表，顺序对应最优特征标签
Returns:
classLabel - 分类结果
"""
def classify(inputTree, featLabels, testVec):
firstStr = next(iter(inputTree)) #获取决策树结点；'有自己的房子'
secondDict = inputTree[firstStr] #下一个字典；{0: {'有工作': {0: 'no', 1: 'yes'}}, 1: 'yes'}
featIndex = featLabels.index(firstStr) # featIndex = 0
for key in secondDict.keys(): #key = 0
if testVec[featIndex] == key:
if type(secondDict[key]).__name__ == 'dict': #如果是字典类型则要继续递归判断
classLabel = classify(secondDict[key], featLabels, testVec)
else: classLabel = secondDict[key] #返回结果值
return classLabel
if __name__ == '__main__':
dataSet, labels = createDataSet()
featLabels = []
myTree = createTree(dataSet, labels, featLabels)
testVec = [0,0] #测试数据
result = classify(myTree, featLabels, testVec)
if result == 'yes':
print('放贷')
if result == 'no':
print('不放贷')