版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/mr_muli/article/details/84699260
- 机器学习之线性回归SVR
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 2 09:53:01 2018
@author: muli
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets,cross_validation,svm
def load_data_regression():
'''
加载用于回归问题的数据集
:return: 一个元组，用于回归问题。
元组元素依次为：训练样本集、测试样本集、训练样本集对应的值、测试样本集对应的值
'''
#使用 scikit-learn 自带的一个糖尿病病人的数据集
diabetes = datasets.load_diabetes()
# 拆分成训练集和测试集，测试集大小为原始数据集大小的 1/4
return cross_validation.train_test_split(diabetes.data,diabetes.target,
test_size=0.25,random_state=0)
def test_LinearSVR(*data):
'''
测试 LinearSVR 的用法
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
:return: None
'''
X_train,X_test,y_train,y_test=data
regr=svm.LinearSVR()
regr.fit(X_train,y_train)
print('Coefficients:%s, intercept %s'%(regr.coef_,regr.intercept_))
print('Score: %.2f' % regr.score(X_test, y_test))
def test_LinearSVR_loss(*data):
'''
测试 LinearSVR 的预测性能随不同损失函数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
:return:
'''
X_train,X_test,y_train,y_test=data
losses=['epsilon_insensitive','squared_epsilon_insensitive']
for loss in losses:
regr=svm.LinearSVR(loss=loss)
regr.fit(X_train,y_train)
print("loss：%s"%loss)
print('Coefficients:%s, intercept %s'%(regr.coef_,regr.intercept_))
print('Score: %.2f' % regr.score(X_test, y_test))
def test_LinearSVR_epsilon(*data):
'''
测试 LinearSVR 的预测性能随 epsilon 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
:return: None
'''
X_train,X_test,y_train,y_test=data
# 等比数列
epsilons=np.logspace(-2,2)
train_scores=[]
test_scores=[]
for epsilon in epsilons:
regr=svm.LinearSVR(epsilon=epsilon,loss='squared_epsilon_insensitive')
regr.fit(X_train,y_train)
train_scores.append(regr.score(X_train, y_train))
test_scores.append(regr.score(X_test, y_test))
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(epsilons,train_scores,label="Training score ",marker='+' )
ax.plot(epsilons,test_scores,label= " Testing score ",marker='o' )
ax.set_title( "LinearSVR_epsilon ")
ax.set_xscale("log")
ax.set_xlabel(r"$\epsilon$")
ax.set_ylabel("score")
ax.set_ylim(-1,1.05)
ax.legend(loc="best",framealpha=0.5)
plt.show()
def test_LinearSVR_C(*data):
'''
测试 LinearSVR 的预测性能随 C 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
:return: None
'''
X_train,X_test,y_train,y_test=data
Cs=np.logspace(-1,2)
train_scores=[]
test_scores=[]
for C in Cs:
regr=svm.LinearSVR(epsilon=0.1,loss='squared_epsilon_insensitive',C=C)
regr.fit(X_train,y_train)
train_scores.append(regr.score(X_train, y_train))
test_scores.append(regr.score(X_test, y_test))
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(Cs,train_scores,label="Training score ",marker='+' )
ax.plot(Cs,test_scores,label= " Testing score ",marker='o' )
ax.set_title( "LinearSVR_C ")
ax.set_xscale("log")
ax.set_xlabel(r"C")
ax.set_ylabel("score")
ax.set_ylim(-1,1.05)
ax.legend(loc="best",framealpha=0.5)
plt.show()
if __name__=="__main__":
X_train,X_test,y_train,y_test=load_data_regression() # 生成用于回归问题的数据集
# test_LinearSVR(X_train,X_test,y_train,y_test) # 调用 test_LinearSVR
# test_LinearSVR_loss(X_train,X_test,y_train,y_test) # 调用 test_LinearSVR_loss
# test_LinearSVR_epsilon(X_train,X_test,y_train,y_test) # 调用 test_LinearSVR_epsilon
test_LinearSVR_C(X_train,X_test,y_train,y_test) # 调用 test_LinearSVR_C
- 如图：
