python 机器学习之支持向量机非线性回归SVR模型

Python  /  管理员 发布于 7年前   249

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 npimport matplotlib.pyplot as pltfrom sklearn import datasets, linear_model,svmfrom sklearn.model_selection import train_test_splitdef 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_LinearSVRtest_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_polytest_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_rbftest_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_sigmoidtest_SVR_sigmoid(X_train,X_test,y_train,y_test)

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。