python实现svm进行三分类_Python实现基于SVM的分类器的方法

本文代码来之《数据分析与挖掘实战》,在此基础上补充完善了一下~

代码是基于SVM的分类器Python实现,原文章节题目和code关系不大,或者说给出已处理好数据的方法缺失、源是图像数据更是不见踪影,一句话就是练习分类器(�㉨�メ)

源代码直接给好了K=30,就试了试怎么选的,挑选规则设定比较单一,有好主意请不吝赐教哟

# -*- coding: utf-8 -*-

"""

Created on Sun Aug 12 12:19:34 2018

@author: Luove

"""

from sklearn import svm

from sklearn import metrics

import pandas as pd

import numpy as np

from numpy.random import shuffle

#from random import seed

#import pickle #保存模型和加载模型

import os

os.getcwd()

os.chdir('D:/Analyze/Python Matlab/Python/BookCodes/Python数据分析与挖掘实战/图书配套数据、代码/chapter9/demo/code')

inputfile = '../data/moment.csv'

data=pd.read_csv(inputfile)

data.head()

data=data.as_matrix()

#seed(10)

shuffle(data) #随机重排,按列,同列重排,因是随机的每次运算会导致结果有差异,可在之前设置seed

n=0.8

train=data[:int(n*len(data)),:]

test=data[int(n*len(data)):,:]

#建模数据 整理

#k=30

m=100

record=pd.DataFrame(columns=['acurrary_train','acurrary_test'])

for k in range(1,m+1):

# k特征扩大倍数,特征值在0-1之间,彼此区分度太小,扩大以提高区分度和准确率

x_train=train[:,2:]*k

y_train=train[:,0].astype(int)

x_test=test[:,2:]*k

y_test=test[:,0].astype(int)

model=svm.SVC()

model.fit(x_train,y_train)

#pickle.dump(model,open('../tmp/svm1.model','wb'))#保存模型

#model=pickle.load(open('../tmp/svm1.model','rb'))#加载模型

#模型评价 混淆矩阵

cm_train=metrics.confusion_matrix(y_train,model.predict(x_train))

cm_test=metrics.confusion_matrix(y_test,model.predict(x_test))

pd.DataFrame(cm_train,index=range(1,6),columns=range(1,6))

accurary_train=np.trace(cm_train)/cm_train.sum() #准确率计算

# accurary_train=model.score(x_train,y_train) #使用model自带的方法求准确率

pd.DataFrame(cm_test,index=range(1,6),columns=range(1,6))

accurary_test=np.trace(cm_test)/cm_test.sum()

record=record.append(pd.DataFrame([accurary_train,accurary_test],index=['accurary_train','accurary_test']).T)

record.index=range(1,m+1)

find_k=record.sort_values(by=['accurary_train','accurary_test'],ascending=False) # 生成一个copy 不改变原变量

find_k[(find_k['accurary_train']>0.95) & (find_k['accurary_test']>0.95) & (find_k['accurary_test']>=find_k['accurary_train'])]

#len(find_k[(find_k['accurary_train']>0.95) & (find_k['accurary_test']>0.95)])

''' k=33

accurary_train accurary_test

33 0.950617 0.95122

'''

''' 计算一下整体

accurary_data

0.95073891625615758

'''

k=33

x_train=train[:,2:]*k

y_train=train[:,0].astype(int)

model=svm.SVC()

model.fit(x_train,y_train)

model.score(x_train,y_train)

model.score(datax_train,datay_train)

datax_train=data[:,2:]*k

datay_train=data[:,0].astype(int)

cm_data=metrics.confusion_matrix(datay_train,model.predict(datax_train))

pd.DataFrame(cm_data,index=range(1,6),columns=range(1,6))

accurary_data=np.trace(cm_data)/cm_data.sum()

accurary_data

REF:

《数据分析与挖掘实战》

源代码及数据需要可自取:https://github.com/Luove/Data

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。

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