python 머 신 러 닝 지원 벡터 머 신 비 선형 회귀 SVR 모델

본 고 는 python 지원 벡터 기 비 선형 회귀 SVR 모델 을 소개 했다.쓸데없는 말 은 하지 않 고 구체 적 으로 다음 과 같다.

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():
  '''
              
  '''
  diabetes = datasets.load_diabetes() #   scikit-learn               
  #           ，               1/4
  return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)

#          SVR  
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))
  
#             
X_train,X_test,y_train,y_test=load_data_regression() 
#    test_LinearSVR
test_SVR_linear(X_train,X_test,y_train,y_test)

def test_SVR_poly(*data):
  '''
           SVR        degree、gamma、coef0    .
  '''
  X_train,X_test,y_train,y_test=data
  fig=plt.figure()
  ###    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)

  ###    gamma，   degree 3， coef0   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)
  ###    r，   gamma   20，degree  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()
  
#    test_SVR_poly
test_SVR_poly(X_train,X_test,y_train,y_test)

def test_SVR_rbf(*data):
  '''
          SVR        gamma      
  '''
  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()
  
#    test_SVR_rbf
test_SVR_rbf(X_train,X_test,y_train,y_test)

def test_SVR_sigmoid(*data):
  '''
     sigmoid    SVR        gamma、coef0    .
  '''
  X_train,X_test,y_train,y_test=data
  fig=plt.figure()

  ###    gammam，   coef0   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)
  ###    r ，   gamma   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()
  
#    test_SVR_sigmoid
test_SVR_sigmoid(X_train,X_test,y_train,y_test)

이상 이 바로 본 고의 모든 내용 입 니 다.여러분 의 학습 에 도움 이 되 고 저 희 를 많이 응원 해 주 셨 으 면 좋 겠 습 니 다.