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代码原文 下面代码参考scikit-learn中文社区#xff0c;链接在上面。 但是由于scikit-learn中文社区上的代码有些地方跑不通#xff0c;故对此代码做了修改#xff0c;输出结果与社区中显示的结果相同。
对弹性网络进行简单的介绍#xff1a; ElasticNet是一个训…弹性网络
代码原文 下面代码参考scikit-learn中文社区链接在上面。 但是由于scikit-learn中文社区上的代码有些地方跑不通故对此代码做了修改输出结果与社区中显示的结果相同。
对弹性网络进行简单的介绍 ElasticNet是一个训练时同时用ℓ1和ℓ2范数进行正则化的线性回归模型lasso是使用ℓ1范数进行正则化的线性回归模型。弹性网络简弹性网络简介弹性网络简
from itertools import cycle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import lasso_path, enet_path
from sklearn import datasetsX, y datasets.load_diabetes(return_X_yTrue)X / X.std(axis0) # Standardize data (easier to set the l1_ratio parameter)
print(------------------------------------)
print(X)
print(------------------------------------)
print(y)
# Compute pathseps 5e-3 # the smaller it is the longer is the pathprint(Computing regularization path using the lasso...)
# alphas_lasso, coefs_lasso, _ lasso_path(X, y, epseps, fit_interceptFalse)
alphas_lasso, coefs_lasso, _ lasso_path(X, y)print(Computing regularization path using the positive lasso...)
# alphas_positive_lasso, coefs_positive_lasso, _ lasso_path(
# X, y, epseps, positiveTrue, fit_interceptFalse)
alphas_positive_lasso, coefs_positive_lasso, _ lasso_path(X, y, epseps, positiveTrue)print(Computing regularization path using the elastic net...)
# alphas_enet, coefs_enet, _ enet_path(
# X, y, epseps, l1_ratio0.8, fit_interceptFalse)
alphas_enet, coefs_enet, _ enet_path(X, y, epseps, l1_ratio0.8)print(Computing regularization path using the positive elastic net...)
# alphas_positive_enet, coefs_positive_enet, _ enet_path(
# X, y, epseps, l1_ratio0.8, positiveTrue, fit_interceptFalse)
alphas_positive_enet, coefs_positive_enet, _ enet_path(X, y, epseps, l1_ratio0.8, positiveTrue)
print(------------------------------------)
print(alphas_positive_enet)
print(------------------------------------)
print(coefs_positive_enet)
# Display resultsplt.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(coefs_lasso, coefs_enet, colors):l1 plt.plot(neg_log_alphas_lasso, coef_l, cc)l2 plt.plot(neg_log_alphas_enet, coef_e, linestyle--, cc)plt.xlabel(-Log(alpha))
plt.ylabel(coefficients)
plt.title(Lasso and Elastic-Net Paths)
plt.legend((l1[-1], l2[-1]), (Lasso, Elastic-Net), loclower 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(coefs_lasso, coefs_positive_lasso, colors):l1 plt.plot(neg_log_alphas_lasso, coef_l, cc)l2 plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle--, cc)plt.xlabel(-Log(alpha))
plt.ylabel(coefficients)
plt.title(Lasso and positive Lasso)
plt.legend((l1[-1], l2[-1]), (Lasso, positive Lasso), loclower 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(coefs_enet, coefs_positive_enet, colors):l1 plt.plot(neg_log_alphas_enet, coef_e, cc)l2 plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle--, cc)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),loclower left)
plt.axis(tight)
plt.show()
