本文介绍了python 支持向量机非线性回归SVR模型，废话不多说，具体如下：

import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets, linear_model,svm

from sklearn.model_selection import train_test_split

def load_data_regression():

'''

加载用于回归问题的数据集

'''

diabetes = datasets.load_diabetes() #使用 scikit-learn 自带的一个糖尿病病人的数据集

# 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 train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)

#支持向量机非线性回归SVR模型

def test_SVR_linear(*data):

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))

# 生成用于回归问题的数据集

X_train,X_test,y_train,y_test=load_data_regression()

# 调用 test_LinearSVR

test_SVR_linear(X_train,X_test,y_train,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.

'''

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)

### 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)

### 测试 r，固定 gamma 为 20，degree为 3 ######

rs=range(0,20)

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)

plt.show()

# 调用 test_SVR_poly

test_SVR_poly(X_train,X_test,y_train,y_test)

def test_SVR_rbf(*data):

'''

测试 高斯核的 SVR 的预测性能随 gamma 参数的影响

'''

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)

plt.show()

# 调用 test_SVR_rbf

test_SVR_rbf(X_train,X_test,y_train,y_test)

def test_SVR_sigmoid(*data):

'''

测试 sigmoid 核的 SVR 的预测性能随 gamma、coef0 的影响.

'''

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()

# 调用 test_SVR_sigmoid

test_SVR_sigmoid(X_train,X_test,y_train,y_test)

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时间： 2019-06-23