## 吴裕雄 python 机器学习——支持向量机线性回归SVR模型

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets, linear_model,svm
from sklearn.model_selection import train_test_split

'''
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)

#支持向量机线性回归SVR模型
def test_LinearSVR(*data):
X_train,X_test,y_train,y_test=data
regr=svm.LinearSVR()
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
# 调用 test_LinearSVR
test_LinearSVR(X_train,X_test,y_train,y_test) 

def test_LinearSVR_loss(*data):
'''
测试 LinearSVR 的预测性能随不同损失函数的影响
'''
X_train,X_test,y_train,y_test=data
losses=['epsilon_insensitive','squared_epsilon_insensitive']
for loss in losses:
regr=svm.LinearSVR(loss=loss)
regr.fit(X_train,y_train)
print("loss：%s"%loss)
print('Coefficients:%s, intercept %s'%(regr.coef_,regr.intercept_))
print('Score: %.2f' % regr.score(X_test, y_test))

# 调用 test_LinearSVR_loss
test_LinearSVR_loss(X_train,X_test,y_train,y_test) 

def test_LinearSVR_epsilon(*data):
'''
测试 LinearSVR 的预测性能随 epsilon 参数的影响
'''
X_train,X_test,y_train,y_test=data
epsilons=np.logspace(-2,2)
train_scores=[]
test_scores=[]
for  epsilon in  epsilons:
regr=svm.LinearSVR(epsilon=epsilon,loss='squared_epsilon_insensitive')
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.plot(epsilons,train_scores,label="Training score ",marker='+' )
ax.plot(epsilons,test_scores,label= " Testing  score ",marker='o' )
ax.set_title( "LinearSVR_epsilon ")
ax.set_xscale("log")
ax.set_xlabel(r"$\epsilon$")
ax.set_ylabel("score")
ax.set_ylim(-1,1.05)
ax.legend(loc="best",framealpha=0.5)
plt.show()

# 调用 test_LinearSVR_epsilon
test_LinearSVR_epsilon(X_train,X_test,y_train,y_test) 

def test_LinearSVR_C(*data):
'''
测试 LinearSVR 的预测性能随 C 参数的影响
'''
X_train,X_test,y_train,y_test=data
Cs=np.logspace(-1,2)
train_scores=[]
test_scores=[]
for  C in  Cs:
regr=svm.LinearSVR(epsilon=0.1,loss='squared_epsilon_insensitive',C=C)
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.plot(Cs,train_scores,label="Training score ",marker='+' )
ax.plot(Cs,test_scores,label= " Testing  score ",marker='o' )
ax.set_title( "LinearSVR_C ")
ax.set_xscale("log")
ax.set_xlabel(r"C")
ax.set_ylabel("score")
ax.set_ylim(-1,1.05)
ax.legend(loc="best",framealpha=0.5)
plt.show()

# 调用 test_LinearSVR_C
test_LinearSVR_C(X_train,X_test,y_train,y_test)