Lasso回归：
#-*- encoding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
np.random.seed(42)
n_samples,n_features=50,200
X=np.random.randn(n_samples,n_features)
coef=3*np.random.randn(n_features)
inds=np.arange(n_features)
np.random.shuffle(inds)
coef[inds[10:]]=0
y=np.dot(X,coef)
##添加噪音：
y+=0.01*np.random.normal((n_samples,))
####分离数据:
n_samples=X.shape[0]
X_train,y_train=X[:int(n_samples/2)],y[:int(n_samples/2)]
X_test,y_test=X[int(n_samples/2):],y[int(n_samples/2):]
###Lasso
from sklearn.linear_model import Lasso
alpha=0.1
lasso=Lasso(alpha=alpha)
y_pred_lasso=lasso.fit(X_train,y_train).predict(X_test)
r2_score_lasso=r2_score(y_test,y_pred_lasso)
print(lasso)
print('r^2 on test data:%f' %r2_score_lasso)
from sklearn.linear_model import ElasticNet
enet=ElasticNet(alpha=alpha,l1_ratio=0.7)
y_pred_enet=enet.fit(X_train,y_train).predict(X_test)
r2_score_enet=r2_score(y_test,y_pred_enet)
print(enet)
print('r^2 on test data:%f' %r2_score_enet)
plt.plot(enet.coef_,label='Elastic net coefficients')
plt.plot(lasso.coef_,label='Lasso coefficients')
plt.plot(coef,'--',label='orginal coefficients' )
plt.legend(loc='best')
plt.title('Lasso R^2:%f,Elastic Net R^2:%f' %(r2_score_lasso,r2_score_enet))
plt.show()
输出：
Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False, positive=False, precompute=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False) r^2 on test data:0.384710 ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0.7, max_iter=1000, normalize=False, positive=False, precompute=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False) r^2 on test data:0.240176
