弹性网络
弹性网络是一个lasso和ridge的结合
下图是官方文档的解释，感觉说的还是很清楚~
Lasso and Elastic Net
from itertools import cycle
from sklearn.linear_model import lasso_path,enet_path
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt
diabetes = datasets.load_diabetes()
x = diabetes.data
y = diabetes.target
x = x/x.std(axis=0)#对于每个变量标准化，(easier to set the l1_ratio parameter)
eps = 5e-3
alphas_lasso,coef_lasso,_ = lasso_path(x,y,eps,fit_intercept = False)
#The alphas and coefs along the path where models are computed.
#注意后面的这个_，是用来填补后面的变量滴
alphas_positive_lasso,coef_positive_lasso,_ = lasso_path(x,y,eps,positive=True
,fit_intercept = False)
#强制把系数约束为正数
alphas_enet,coef_enet,_ = enet_path(x,y,eps=eps,l1_ratio=0.8,fit_intercept=False)
alphas_positive_enet,coef_positive_enet,_ = enet_path(x,y,eps=eps,
l1_ratio=0.8,
fit_intercept=False,
positive=True)
plt.figure(1)
colors = cycle(['b','r','g','c','k'])
neg_log_alphas_lasso = -np.log10(alphas_lasso)
neg_log_alphas_enet = -np.log10(alphas_enet)
for coef_l,coef_e,c in zip(coef_lasso,coef_enet,colors):
l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle='--', c=c)
plt.xlabel('-Log(alpha)')
plt.ylabel('coefficients')
plt.title('Lasso and Elastic-Net Paths')
plt.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='lower left')
plt.axis('tight')
plt.figure(2)
neg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)
for coef_l, coef_pl, c in zip(coef_lasso, coef_positive_lasso, colors):
l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle='--', c=c)
plt.xlabel('-Log(alpha)')
plt.ylabel('coefficients')
plt.title('Lasso and positive Lasso')
plt.legend((l1[-1], l2[-1]), ('Lasso', 'positive Lasso'), loc='lower left')
plt.axis('tight')
plt.figure(3)
neg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)
for (coef_e, coef_pe, c) in zip(coef_enet, coef_positive_enet, colors):
l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)
l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle='--', c=c)
plt.xlabel('-Log(alpha)')
plt.ylabel('coefficients')
plt.title('Elastic-Net and positive Elastic-Net')
plt.legend((l1[-1], l2[-1]), ('Elastic-Net', 'positive Elastic-Net'),
loc='lower left')
plt.axis('tight')
plt.show()
