Python编程实现ID3算法,使用西瓜数据集产生结果
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2022-04-02 11:11:21
数据集和注意的地方,我已经发在BP算法第一篇,你们看一下吧。具体代码,不分析了,网上太多,有问题请留言。给我点个赞加个关注吧。上代码:# encoding:utf-8import pandas as pdimport numpy as npclass DecisionTree: def __init__(self): self.model = None def calEntropy(self, y): # 计算熵 valRate =...
数据集和注意的地方,我已经发在BP算法第一篇,你们看一下吧。
具体代码,不分析了,网上太多,有问题请留言。
给我点个赞加个关注吧。
上代码:
# encoding:utf-8
import pandas as pd
import numpy as np
class DecisionTree:
def __init__(self):
self.model = None
def calEntropy(self, y): # 计算熵
valRate = y.value_counts().apply(lambda x: x / y.size) # 频次汇总 得到各个特征对应的概率
valEntropy = np.inner(valRate, np.log2(valRate)) * -1
return valEntropy
def fit(self, xTrain, yTrain=pd.Series()):
if yTrain.size == 0: # 如果不传,自动选择最后一列作为分类标签
yTrain = xTrain.iloc[:, -1]
xTrain = xTrain.iloc[:, :len(xTrain.columns) - 1]
self.model = self.buildDecisionTree(xTrain, yTrain)
return self.model
def buildDecisionTree(self, xTrain, yTrain):
propNamesAll = xTrain.columns
# print(propNamesAll)
yTrainCounts = yTrain.value_counts()
if yTrainCounts.size == 1:
# print('only one class', yTrainCounts.index[0])
return yTrainCounts.index[0]
entropyD = self.calEntropy(yTrain)
maxGain = None
maxEntropyPropName = None
for propName in propNamesAll:
propDatas = xTrain[propName]
propClassSummary = propDatas.value_counts().apply(lambda x: x / propDatas.size) # 频次汇总 得到各个特征对应的概率
sumEntropyByProp = 0
for propClass, dvRate in propClassSummary.items():
yDataByPropClass = yTrain[xTrain[propName] == propClass]
entropyDv = self.calEntropy(yDataByPropClass)
sumEntropyByProp += entropyDv * dvRate
gainEach = entropyD - sumEntropyByProp
if maxGain == None or gainEach > maxGain:
maxGain = gainEach
maxEntropyPropName = propName
# print('select prop:', maxEntropyPropName, maxGain)
propDatas = xTrain[maxEntropyPropName]
propClassSummary = propDatas.value_counts().apply(lambda x: x / propDatas.size) # 频次汇总 得到各个特征对应的概率
retClassByProp = {}
for propClass, dvRate in propClassSummary.items():
whichIndex = xTrain[maxEntropyPropName] == propClass
if whichIndex.size == 0:
continue
xDataByPropClass = xTrain[whichIndex]
yDataByPropClass = yTrain[whichIndex]
del xDataByPropClass[maxEntropyPropName] # 删除已经选择的属性列
# print(propClass)
# print(pd.concat([xDataByPropClass, yDataByPropClass], axis=1))
retClassByProp[propClass] = self.buildDecisionTree(xDataByPropClass, yDataByPropClass)
return {'Node': maxEntropyPropName, 'Edge': retClassByProp}
def predictBySeries(self, modelNode, data):
if not isinstance(modelNode, dict):
return modelNode
nodePropName = modelNode['Node']
prpVal = data.get(nodePropName)
for edge, nextNode in modelNode['Edge'].items():
if prpVal == edge:
return self.predictBySeries(nextNode, data)
return None
def predict(self, data):
if isinstance(data, pd.Series):
return self.predictBySeries(self.model, data)
return data.apply(lambda d: self.predictBySeries(self.model, d), axis=1)
dataTrain = pd.read_csv(r"C:\Users\杨涵文\PycharmProjects\BP算法\方法一\data\table_4.2.csv", encoding="utf-8")
decisionTree = DecisionTree()
treeData = decisionTree.fit(dataTrain)
print(pd.DataFrame({'预测值': decisionTree.predict(dataTrain), '正取值': dataTrain.iloc[:, -1]}))
import json
print(json.dumps(treeData, ensure_ascii=False))
运行结果:
是不是感觉很不错,ID3算法个人感觉更好,哈哈,看自己呀。
本文地址:https://blog.csdn.net/weixin_46211269/article/details/109641354