mission details
Task for this level: Classify using decision trees
related information
In order to complete this task, you need to master: 1. Use decision trees for classification
Classification using decision trees
After constructing the decision tree based on the training data, we can use it to classify the actual data. When performing data classification, a decision tree is required along with the label vectors used to construct the tree. Then, the program compares the test data with the values on the decision tree, and performs the process recursively until entering the leaf node; finally, the test data is defined as the type to which the leaf node belongs. The classification function using a decision tree is as follows:
"""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"""# 函数说明:使用决策树分类def classify(inputTree, featLabels, testVec):firstStr = next(iter(inputTree)) #获取决策树结点secondDict = inputTree[firstStr] #下一个字典featIndex = featLabels.index(firstStr)for key in secondDict.keys():if testVec[featIndex] == key:if type(secondDict[key]).__name__ == 'dict':classLabel = classify(secondDict[key], featLabels, testVec)else: classLabel = secondDict[key]return classLabel
Programming requirements
Follow the prompts, add code in the editor on the right, and use decision tree classification
Test instruction
The platform will test the code you write:
Start your mission and wish you success!
The complete code is as follows:
# -*- coding: UTF-8 -*-
from math import log
import operator
"""
Parameters:
dataSet - 数据集
Returns:
shannonEnt - 经验熵(香农熵)
"""
# 函数说明:计算给定数据集的经验熵(香农熵)
def calcShannonEnt(dataSet):
numEntires = len(dataSet) #返回数据集的行数
labelCounts = {} #保存每个标签(Label)出现次数的字典
for featVec in dataSet: #对每组特征向量进行统计
currentLabel = featVec[-1] #提取标签(Label)信息
if currentLabel not in labelCounts.keys(): #如果标签(Label)没有放入统计次数的字典,添加进去
labelCounts[currentLabel] = 0
labelCounts[currentLabel] += 1 #Label计数
shannonEnt = 0.0 #经验熵(香农熵)
for key in labelCounts: #计算香农熵
prob = float(labelCounts[key]) / numEntires#选择该标签(Label)的概率
shannonEnt -= prob * log(prob, 2) #利用公式计算
return shannonEnt #返回经验熵(香农熵)
"""
Parameters:
无
Returns:
dataSet - 数据集
labels - 特征标签
"""
# 函数说明:创建测试数据集
def createDataSet():
dataSet = [[0, 0, 0, 0, 'no'],#数据集
[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']#特征标签
return dataSet, labels#返回数据集和分类属性
"""
Parameters:
dataSet - 待划分的数据集
axis - 划分数据集的特征
value - 需要返回的特征的值
Returns:
无
"""
# 函数说明:按照给定特征划分数据集
def splitDataSet(dataSet, axis, value):
retDataSet = [] #创建返回的数据集列表
for featVec in dataSet: #遍历数据集
if featVec[axis] == value:
reducedFeatVec = featVec[:axis] #去掉axis特征
reducedFeatVec.extend(featVec[axis+1:])#将符合条件的添加到返回的数据集
retDataSet.append(reducedFeatVec)
return retDataSet #返回划分后的数据集
"""
Parameters:
dataSet - 数据集
Returns:
bestFeature - 信息增益最大的(最优)特征的索引值
"""
# 函数说明:选择最优特征
def chooseBestFeatureToSplit(dataSet):
numFeatures = len(dataSet[0]) - 1 #特征数量
baseEntropy = calcShannonEnt(dataSet) #计算数据集的香农熵
bestInfoGain = 0.0 #信息增益
bestFeature = -1 #最优特征的索引值
for i in range(numFeatures): #遍历所有特征
#获取dataSet的第i个所有特征
featList = [example[i] for example in dataSet]
uniqueVals = set(featList) #创建set集合{},元素不可重复
newEntropy = 0.0 #经验条件熵
for value in uniqueVals: #计算信息增益
subDataSet = splitDataSet(dataSet, i, value) #subDataSet划分后的子集
prob = len(subDataSet) / float(len(dataSet)) #计算子集的概率
newEntropy += prob * calcShannonEnt(subDataSet)#根据公式计算经验条件熵
infoGain = baseEntropy - newEntropy #信息增益
# print("第%d个特征的增益为%.3f" % (i, infoGain)) #打印每个特征的信息增益
if (infoGain > bestInfoGain): #计算信息增益
bestInfoGain = infoGain #更新信息增益，找到最大的信息增益
bestFeature = i #记录信息增益最大的特征的索引值
return bestFeature #返回信息增益最大的特征的索引值
"""
Parameters:
classList - 类标签列表
Returns:
sortedClassCount[0][0] - 出现此处最多的元素(类标签)
"""
# 函数说明:统计classList中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
"""
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
"""
# 函数说明:创建决策树
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
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
return majorityCnt(classList)
bestFeat = chooseBestFeatureToSplit(dataSet) #选择最优特征
bestFeatLabel = labels[bestFeat] #最优特征的标签
featLabels.append(bestFeatLabel)
myTree = {bestFeatLabel:{}} #根据最优特征的标签生成树
del(labels[bestFeat]) #删除已经使用特征标签
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)
return myTree
"""
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
"""
# 函数说明:使用决策树分类
def classify(inputTree, featLabels, testVec):
firstStr = next(iter(inputTree)) #获取决策树结点
secondDict = inputTree[firstStr] #下一个字典
featIndex = featLabels.index(firstStr)
for key in secondDict.keys():
if testVec[featIndex] == key:
if type(secondDict[key]).__name__ == 'dict':
classLabel = classify(secondDict[key], featLabels, testVec)
else: classLabel = secondDict[key]
return classLabel
if __name__ == '__main__':
#########
#请输入你的代码
dataSet, labels = createDataSet() #得到数据集
featLabels = []
myTree = createTree(dataSet, labels, featLabels) #创造树
testVec = [0,1] #测试数据
result = classify(myTree, featLabels, testVec) #进行分类
#########
if result == 'yes':
print('放贷')
if result == 'no':
print('不放贷')