决策树代码（数据集以西瓜集为例我自己手录）

决策树实验

• @[TOC](决策树实验)
• 前言
• 一、使用步骤
• 二、结果

---

前言

决策树理论数据这里不讲，只把我的代码贴出来。代码一部分来源机器学习实战，详细的注释是我自己加的。另一部分源码我自己写的（处理西瓜集的部分），如有错误欢迎指正。

---

一、使用步骤

1.源码

代码如下（示例）：

from math import log
import operator

def convert(filename):
    fr = open(filename,encoding="utf-8")
    arrayOfLines = fr.readlines()
    #print(arrayOfLines)
    labels = arrayOfLines[0]
    attrubute = labels.strip().split(",")
    del(attrubute[0])
    del(attrubute[-1])
    del(arrayOfLines[0])
    fileLineNumber = len(arrayOfLines)
    for i in range(fileLineNumber):
        arrayOfLines[i] = arrayOfLines[i].strip().split(',')
        del(arrayOfLines[i][0])
    return arrayOfLines,attrubute



#定义函数CalShannonEnt()用于计算样本空间的信息熵
def calcShannonEnt(dataSet):
    numEntries = len(dataSet) #numEntries 计算数据集中样本数
    labelCounts = {}          #创建字典用来存储记录不同样本的个数
    for featureVector in dataSet:     #遍历结构体dataSet中的每一行
        currentLabel = featureVector[-1]   #获取每一行的最后一个元素，也就是标签
        if currentLabel not in labelCounts.keys():      #判断当前标签是否在字典中的key值中
            labelCounts[currentLabel] = 0  #初始化字典中标签和数量的键值对
        labelCounts[currentLabel] += 1   #如果dataSet中的标签在字典的key值中value值加一
    shannonEnt = 0.0                               #初始化信息熵
    for key in labelCounts:                         #判断key在
        prob = float(labelCounts[key])/numEntries   #计算不同样本在数据中的占比
        shannonEnt -= prob * log(prob,2)
    return shannonEnt

#创建一个数据集用于测试calcShannonEnt
#返回值有两个，数据集和标签
'''def createDataSet():
    dataSet = [[1,1,'yes'],
               [1,1,'yes'],
               [1,0,'no'],
               [0,1,'no'],
               [0,1,'no']]
    labels = ['no surfacing','flippers']
    return dataSet,labels'''

def splitDataSet(dataSet,axis,value):
    returnDataSet = []
    for featureVector in dataSet:
        if featureVector[axis] == value:
            reduceFeatureVector = featureVector[:axis]
            reduceFeatureVector.extend(featureVector[axis+1:])
            returnDataSet.append(reduceFeatureVector)
    return returnDataSet

#选择最好的划分数据集方式
def chooseBestFeatureToSplit(dataSet):
    numFeatures = len(dataSet[0]) -1  #计算特征向量的长度，等于矩阵行长减去一
    #numFeature = dataSet.shape[1] - 1
    baseEntropy = calcShannonEnt(dataSet)
    bestInfoGain = 0.0
    bestFeature = -1
    for i in range(numFeatures):
        featureList = [example[i]for example in dataSet]
        uniqueVals  = set(featureList)
        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
        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):
    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)
    bestFeature = chooseBestFeatureToSplit(dataSet)
    bestFeatureLabel = labels[bestFeature]
    myTree = {bestFeatureLabel:{}}
    del(labels[bestFeature])
    featureValues = [example[bestFeature] for example in dataSet]
    uniqueValues = set(featureValues)
    for value in uniqueValues:
        subLabels = labels[:]
        myTree[bestFeatureLabel][value] = createTree(splitDataSet(dataSet,bestFeature,value),subLabels)
    return myTree

dataSet,labels = convert("西瓜集.csv")
#print(dataSet)
#print(labels)
print(createTree(dataSet,labels))

2.数据集

代码如下（示例）：

链接：https://pan.baidu.com/s/1hHdeRBXLgO89SekTT7UZPA
提取码：fw54

---

二、结果

本文地址：https://blog.csdn.net/qq_41893089/article/details/110499143