## 吴裕雄 python 机器学习——ElasticNet回归

import numpy as np
import matplotlib.pyplot as plt

from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split

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

#ElasticNet回归
def test_ElasticNet(*data):
X_train,X_test,y_train,y_test=data
regr = linear_model.ElasticNet()
regr.fit(X_train, y_train)
print('Coefficients:%s, intercept %.2f'%(regr.coef_,regr.intercept_))
print("Residual sum of squares: %.2f"% np.mean((regr.predict(X_test) - y_test) ** 2))
print('Score: %.2f' % regr.score(X_test, y_test))

# 产生用于回归问题的数据集
# 调用 test_ElasticNet
test_ElasticNet(X_train,X_test,y_train,y_test)

def test_ElasticNet_alpha_rho(*data):
X_train,X_test,y_train,y_test=data
alphas=np.logspace(-2,2)
rhos=np.linspace(0.01,1)
scores=[]
for alpha in alphas:
for rho in rhos:
regr = linear_model.ElasticNet(alpha=alpha,l1_ratio=rho)
regr.fit(X_train, y_train)
scores.append(regr.score(X_test, y_test))
## 绘图
alphas, rhos = np.meshgrid(alphas, rhos)
scores=np.array(scores).reshape(alphas.shape)
fig=plt.figure()
ax=Axes3D(fig)
surf = ax.plot_surface(alphas, rhos, scores, rstride=1, cstride=1, cmap=cm.jet,linewidth=0, antialiased=False)
fig.colorbar(surf, shrink=0.5, aspect=5)
ax.set_xlabel(r"$\alpha$")
ax.set_ylabel(r"$\rho$")
ax.set_zlabel("score")
ax.set_title("ElasticNet")
plt.show()

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