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/84700414
- 机器学习之非线性回归SVR
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 30 20:56:54 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
'''
diabetes = datasets.load_diabetes() #使用 scikit-learn 自带的一个糖尿病病人的数据集
return cross_validation.train_test_split(diabetes.data,diabetes.target,
test_size=0.25,random_state=0)# 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
def test_SVR_linear(*data):
'''
测试 SVR 的用法。这里使用最简单的线性核
: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.SVR(kernel='linear')
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_SVR_poly(*data):
'''
测试 多项式核的 SVR 的预测性能随 degree、gamma、coef0 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
fig=plt.figure()
### 测试 degree ####
degrees=range(1,20)
train_scores=[]
test_scores=[]
for degree in degrees:
regr=svm.SVR(kernel='poly',degree=degree,coef0=1)
regr.fit(X_train,y_train)
train_scores.append(regr.score(X_train,y_train))
test_scores.append(regr.score(X_test, y_test))
ax=fig.add_subplot(1,3,1)
ax.plot(degrees,train_scores,label="Training score ",marker='+' )
ax.plot(degrees,test_scores,label= " Testing score ",marker='o' )
ax.set_title( "SVR_poly_degree r=1")
ax.set_xlabel("p")
ax.set_ylabel("score")
ax.set_ylim(-1,1.)
ax.legend(loc="best",framealpha=0.5)
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
### 测试 gamma，固定 degree为3， coef0 为 1 ####
gammas=range(1,40)
train_scores=[]
test_scores=[]
for gamma in gammas:
regr=svm.SVR(kernel='poly',gamma=gamma,degree=3,coef0=1)
regr.fit(X_train,y_train)
train_scores.append(regr.score(X_train,y_train))
test_scores.append(regr.score(X_test, y_test))
ax=fig.add_subplot(1,3,2)
ax.plot(gammas,train_scores,label="Training score ",marker='+' )
ax.plot(gammas,test_scores,label= " Testing score ",marker='o' )
ax.set_title( "SVR_poly_gamma r=1")
ax.set_xlabel(r"$\gamma$")
ax.set_ylabel("score")
ax.set_ylim(-1,1)
ax.legend(loc="best",framealpha=0.5)
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
### 测试 r，固定 gamma 为 20，degree为 3 ######
rs=range(-4,20)
train_scores=[]
test_scores=[]
for r in rs:
regr=svm.SVR(kernel='poly',gamma=20,degree=3,coef0=r)
regr.fit(X_train,y_train)
train_scores.append(regr.score(X_train,y_train))
test_scores.append(regr.score(X_test, y_test))
ax=fig.add_subplot(1,3,3)
ax.plot(rs,train_scores,label="Training score ",marker='+' )
ax.plot(rs,test_scores,label= " Testing score ",marker='o' )
ax.set_title( "SVR_poly_r gamma=20 degree=3")
ax.set_xlabel(r"r")
ax.set_ylabel("score")
ax.set_ylim(-1,1.)
ax.legend(loc="best",framealpha=0.5)
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
def test_SVR_rbf(*data):
'''
测试 高斯核的 SVR 的预测性能随 gamma 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
gammas=range(1,20)
train_scores=[]
test_scores=[]
for gamma in gammas:
regr=svm.SVR(kernel='rbf',gamma=gamma)
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(gammas,train_scores,label="Training score ",marker='+' )
ax.plot(gammas,test_scores,label= " Testing score ",marker='o' )
ax.set_title( "SVR_rbf")
ax.set_xlabel(r"$\gamma$")
ax.set_ylabel("score")
ax.set_ylim(-1,1)
ax.legend(loc="best",framealpha=0.5)
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
def test_SVR_sigmoid(*data):
'''
测试 sigmoid 核的 SVR 的预测性能随 gamma、coef0 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
fig=plt.figure()
### 测试 gammam，固定 coef0 为 0.01 ####
gammas=np.logspace(-1,3)
train_scores=[]
test_scores=[]
for gamma in gammas:
regr=svm.SVR(kernel='sigmoid',gamma=gamma,coef0=0.01)
regr.fit(X_train,y_train)
train_scores.append(regr.score(X_train,y_train))
test_scores.append(regr.score(X_test, y_test))
ax=fig.add_subplot(1,2,1)
ax.plot(gammas,train_scores,label="Training score ",marker='+' )
ax.plot(gammas,test_scores,label= " Testing score ",marker='o' )
ax.set_title( "SVR_sigmoid_gamma r=0.01")
ax.set_xscale("log")
ax.set_xlabel(r"$\gamma$")
ax.set_ylabel("score")
ax.set_ylim(-1,1)
ax.legend(loc="best",framealpha=0.5)
### 测试 r ，固定 gamma 为 10 ######
rs=np.linspace(0,5)
train_scores=[]
test_scores=[]
for r in rs:
regr=svm.SVR(kernel='sigmoid',coef0=r,gamma=10)
regr.fit(X_train,y_train)
train_scores.append(regr.score(X_train,y_train))
test_scores.append(regr.score(X_test, y_test))
ax=fig.add_subplot(1,2,2)
ax.plot(rs,train_scores,label="Training score ",marker='+' )
ax.plot(rs,test_scores,label= " Testing score ",marker='o' )
ax.set_title( "SVR_sigmoid_r gamma=10")
ax.set_xlabel(r"r")
ax.set_ylabel("score")
ax.set_ylim(-1,1)
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_SVR_linear(X_train,X_test,y_train,y_test) # 调用 test_SVR_linear
# test_SVR_poly(X_train,X_test,y_train,y_test) # 调用 test_SVR_poly
# test_SVR_rbf(X_train,X_test,y_train,y_test) # 调用 test_SVR_rbf
test_SVR_sigmoid(X_train,X_test,y_train,y_test) # 调用 test_SVR_sigmod
- 如图：
