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python 機(jī)器學(xué)習(xí)之支持向量機(jī)非線性回歸SVR模型

更新時(shí)間：2019年06月26日 09:56:25 作者：吳裕雄

這篇文章主要介紹了python 機(jī)器學(xué)習(xí)之支持向量機(jī)非線性回歸SVR模型，文中通過示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

本文介紹了python 支持向量機(jī)非線性回歸SVR模型，廢話不多說，具體如下：

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():
  '''
  加載用于回歸問題的數(shù)據(jù)集
  '''
  diabetes = datasets.load_diabetes() #使用 scikit-learn 自帶的一個(gè)糖尿病病人的數(shù)據(jù)集
  # 拆分成訓(xùn)練集和測(cè)試集，測(cè)試集大小為原始數(shù)據(jù)集大小的 1/4
  return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)

#支持向量機(jī)非線性回歸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))
  
# 生成用于回歸問題的數(shù)據(jù)集
X_train,X_test,y_train,y_test=load_data_regression() 
# 調(diào)用 test_LinearSVR
test_SVR_linear(X_train,X_test,y_train,y_test)

def test_SVR_poly(*data):
  '''
  測(cè)試 多項(xiàng)式核的 SVR 的預(yù)測(cè)性能隨 degree、gamma、coef0 的影響.
  '''
  X_train,X_test,y_train,y_test=data
  fig=plt.figure()
  ### 測(cè)試 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)

  ### 測(cè)試 gamma，固定 degree為3， coef0 為 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)
  ### 測(cè)試 r，固定 gamma 為 20，degree為 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()
  
# 調(diào)用 test_SVR_poly
test_SVR_poly(X_train,X_test,y_train,y_test)

def test_SVR_rbf(*data):
  '''
  測(cè)試 高斯核的 SVR 的預(yù)測(cè)性能隨 gamma 參數(shù)的影響
  '''
  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()
  
# 調(diào)用 test_SVR_rbf
test_SVR_rbf(X_train,X_test,y_train,y_test)

def test_SVR_sigmoid(*data):
  '''
  測(cè)試 sigmoid 核的 SVR 的預(yù)測(cè)性能隨 gamma、coef0 的影響.
  '''
  X_train,X_test,y_train,y_test=data
  fig=plt.figure()

  ### 測(cè)試 gammam，固定 coef0 為 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)
  ### 測(cè)試 r ，固定 gamma 為 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()
  
# 調(diào)用 test_SVR_sigmoid
test_SVR_sigmoid(X_train,X_test,y_train,y_test)