决策树空气分析

最新推荐文章于 2023-07-06 16:54:37 发布

原创最新推荐文章于 2023-07-06 16:54:37 发布 · 640 阅读

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本文介绍了一个使用决策树分类器预测空气质量等级的数据科学项目。通过对原始数据进行预处理，并将其转换为数值形式，然后利用 sklearn 库中的 DecisionTreeClassifier 进行模型训练及评估。

数据处理

# -*- coding: utf-8 -*-
"""
Created on Mon Apr 24 16:29:07 2017

@author: Administrator
"""

import pandas as pd

inputfile = 'F:/data/chapter9/demo/data/consider.xls' #数据文件
outputfile= 'F:/data/chapter9/demo/data/consider_o.xls'
data=pd.read_excel(inputfile)
'''
for i in range(data['空气等级'].size):
    if data['空气等级'][i]=='I':
        data['空气等级'][i]=1
    elif data['空气等级'][i]=='II':
        data['空气等级'][i]=2
    elif data['空气等级'][i]=='III':
        data['空气等级'][i]=3
    elif data['空气等级'][i]=='IV':
        data['空气等级'][i]=4
    elif data['空气等级'][i]=='V':
        data['空气等级'][i]=5
    elif data['空气等级'][i]=='VI':
        data['空气等级'][i]=6
    elif data['空气等级'][i]=='VII':
        data['空气等级'][i]=7
'''
data['空气等级'][data['空气等级']=='I']=1
data['空气等级'][data['空气等级']=='II']=2
data['空气等级'][data['空气等级']=='III']=3
data['空气等级'][data['空气等级']=='IV']=4
data['空气等级'][data['空气等级']=='V']=5
data['空气等级'][data['空气等级']=='VI']=6
data['空气等级'][data['空气等级']=='VII']=7

data.to_excel(outputfile)

模型训练

# -*- coding: utf-8 -*-
"""
Created on Mon Apr 24 16:58:23 2017

@author: Administrator
"""

#-*- coding: utf-8 -*-
import pandas as pd
from sklearn.tree import DecisionTreeClassifier as DTC

inputfile = 'F:/data/chapter9/demo/data/consider_o.xls' #数据文件
outputfile1 = 'F:/data/chapter9/demo/tmp/train_consider.xls' #训练样本混淆矩阵保存路径
outputfile2 = 'F:/data/chapter9/demo/tmp/test_consider.xls' #测试样本混淆矩阵保存路径

data = pd.read_excel(inputfile) #读取数据，指定编码为gbk
data = data.as_matrix()

from numpy.random import shuffle #引入随机函数
shuffle(data) #随机打乱数据
data_train = data[:int(0.8*len(data)), :] #选取前80%为训练数据
data_test = data[int(0.8*len(data)):, :] #选取前20%为测试数据

#构造特征和标签
x_train = data_train[:, 0:5]*30
y_train = data_train[:, 6].astype(int)
x_test = data_test[:, 0:5]*30
y_test = data_test[:, 6].astype(int)

model=DTC(criterion='entropy')
model.fit(x_train,y_train)

print(model.score(x_test,y_test))
#导入输出相关的库，生成混淆矩阵
from sklearn import metrics
cm_train = metrics.confusion_matrix(y_train, model.predict(x_train)) #训练样本的混淆矩阵
cm_test = metrics.confusion_matrix(y_test, model.predict(x_test)) #测试样本的混淆矩阵

#保存结果
pd.DataFrame(cm_train, index = range(1, 8), columns = range(1, 8)).to_excel(outputfile1)
pd.DataFrame(cm_test, index = range(1, 8), columns = range(1, 8)).to_excel(outputfile2)
print(model.score(x_train,y_train))