决策树基本 CART Python手写实现

参考资料:
https://blog.csdn.net/weixin_45666566/article/details/107954454
https://blog.csdn.net/Elenstone/article/details/105328111

代码如下:
python 复制代码
#-*- coding:utf-8 -*-
import numpy as np
import pandas as pd
import operator

def loadDataSet():
    csv = pd.read_csv(filepath_or_buffer=r'D:/PythonData/决策树.csv')
    dataSet = np.array(csv)
    labels = np.array(csv.columns)[:4]
    targets = sorted(np.unique(dataSet[:,-1:].flatten()), reverse=True)
    return dataSet, labels, targets

def calcProbabilityEnt(dataSet, targets):
    numEntries = len(dataSet)  # 数据条数
    feaCounts = 0
    fea1 = targets[0]
    for featVec in dataSet:
        if featVec[-1] == fea1:
            feaCounts +=1

    probabilityEnt = float(feaCounts) / numEntries
    return probabilityEnt    


def splitDataSet(dataSet, index, value):
    retDataSet = []
    noRetDataSet = []
    for featVec in dataSet:
        if featVec[index]  == value:
            retDataSet.append(np.concatenate((featVec[:index],featVec[index+1:])))
        if featVec[index]  != value:
            noRetDataSet.append(np.concatenate((featVec[:index],featVec[index+1:])))

    return retDataSet,noRetDataSet

def chooseBestFeatureToSplit(dataSet, targets):
    numFeatures = len(dataSet[0]) - 1
    if numFeatures == 1:
        return 0
    bestGini = 1
    bestFeatureIndex = -1
    for i in range(numFeatures):
        # 每一列中的唯一值集合
        uniqueVals = set(example[i] for example in dataSet)
        feaGini = 0
        for value in uniqueVals:
            subDataSet,noSubDataSet = splitDataSet(dataSet=dataSet, index=i,value=value)
            prod = len(subDataSet) / float(len(dataSet))
            noPord = len(noSubDataSet) / float(len(dataSet))
            probabilityEnt = calcProbabilityEnt(subDataSet, targets)
            noProbabilityEnt = calcProbabilityEnt(noSubDataSet,targets)
            feaGini = round(prod * 2 * probabilityEnt * (1 - probabilityEnt) +  (noPord * (2 * noProbabilityEnt * (1 - noProbabilityEnt))),2)
    
            if bestGini > feaGini:
                bestGini = feaGini
                bestFeatureIndex = i
    
    return bestFeatureIndex

def majorityCnt(classList):
    classCount = {}
    for vote in classList:
        try:
            classCount[vote] += 1
        except KeyError:
            classCount[vote] = 1
    
    sortedClassCount = sorted(iterable=classCount.items(),key=operator.itemgetter(1),reverse=True)
    return sortedClassCount[0][0]

def createTree(dataSet, labels,targets):
    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=classList)

    bestFeatIndex  = chooseBestFeatureToSplit(dataSet=dataSet,targets=targets)
    bestFeatLabel = labels[bestFeatIndex]
    np.delete(labels,bestFeatIndex)
    uniqueVals = set(example[bestFeatIndex] for example in dataSet) # 选出最优特征对应属性的唯一值
    myTree = {bestFeatLabel:{}} # 分类结果以字典形式保存
    for value in uniqueVals:
        subLabels = labels[:] # 深拷贝,拷贝后的值与原值无关(普通复制为浅拷贝,对原值或拷贝后的值的改变互相影响)
        subDataSet,noSubDataSet = splitDataSet(dataSet,bestFeatIndex,value)
        myTree[bestFeatLabel][value] = createTree(subDataSet,subLabels,targets) # 递归调用创建决策树
    return myTree
    


if __name__=='__main__':
    dataSet,labels,targets = loadDataSet()
    print(createTree(dataSet,labels,targets))
运行如果如下:
shell 复制代码
PS D:\PythonWorkSpace> & E:/anaconda3/python.exe d:/PythonWorkSpace/DecisionTreeDemo.py
{'有自己的房子': {'否': {'有工作': {'否': '不同意', '是': '同意'}}, '是': '同意'}}
相关推荐
Madison-No717 分钟前
【C++】探秘vector的底层实现
java·c++·算法
Swift社区26 分钟前
LeetCode 401 - 二进制手表
算法·leetcode·ssh
派大星爱吃猫27 分钟前
顺序表算法题(LeetCode)
算法·leetcode·职场和发展
liu****36 分钟前
8.list的模拟实现
linux·数据结构·c++·算法·list
默默coding的程序猿1 小时前
3.git的分支携带问题是什么?怎么解决?
java·git·python·svn·gitee·github·intellij-idea
地平线开发者1 小时前
征程 6 | 征程 6 工具链如何支持 Matmul/Conv 双 int16 输入量化?
算法·自动驾驶
程序员大雄学编程2 小时前
「深度学习笔记4」深度学习优化算法完全指南:从梯度下降到Adam的实战详解
笔记·深度学习·算法·机器学习
新子y3 小时前
【小白笔记】PyTorch 和 Python 基础的这些问题
pytorch·笔记·python
我是李武涯3 小时前
PyTorch DataLoader 高级用法
人工智能·pytorch·python
Lynnxiaowen3 小时前
今天我们开始学习python语句和模块
linux·运维·开发语言·python·学习