标准化Python Lasso和R glmnet中的X不同？

我试图用Python的scikit learn和R的glmnet来拟合lasso得到同样的结果

如果我在Python中指定“normalize=True”，在R中指定“standarize=T”，它们会给出相同的结果

Python:

from sklearn.linear_model import Lasso
X = np.array([[1, 1, 2], [3, 4, 2], [6, 5, 2], [5, 5, 3]])
y = np.array([1, 0, 0, 1])
reg = Lasso(alpha =0.01, fit_intercept = True, normalize =True)
reg.fit(X, y)
np.hstack((reg.intercept_, reg.coef_))

Out[95]: array([-0.89607695,  0.        , -0.24743375,  1.03286824])

reg_glmnet = glmnet(X, y, alpha = 1, lambda = 0.02,standardize = T)
coef(reg_glmnet)

4 x 1 sparse Matrix of class "dgCMatrix"
                    s0
(Intercept) -0.8960770
V1           .        
V2          -0.2474338
V3           1.0328682

from sklearn.linear_model import Lasso
Z = np.array([[1, 1, 2], [3, 4, 2], [6, 5, 2], [5, 5, 3]])
y = np.array([1, 0, 0, 1])
reg = Lasso(alpha =0.01, fit_intercept = True, normalize =False)
reg.fit(Z, y)
np.hstack((reg.intercept_, reg.coef_))

Out[96]: array([-0.88      ,  0.09384212, -0.36159299,  1.05958478])

reg_glmnet = glmnet(X, y, alpha = 1, lambda = 0.02,standardize = F)
coef(reg_glmnet)

4 x 1 sparse Matrix of class "dgCMatrix"
                     s0
(Intercept) -0.76000000
V1           0.04441697
V2          -0.29415542
V3           0.97623074

R:

from sklearn.linear_model import Lasso
X = np.array([[1, 1, 2], [3, 4, 2], [6, 5, 2], [5, 5, 3]])
y = np.array([1, 0, 0, 1])
reg = Lasso(alpha =0.01, fit_intercept = True, normalize =True)
reg.fit(X, y)
np.hstack((reg.intercept_, reg.coef_))

Out[95]: array([-0.89607695,  0.        , -0.24743375,  1.03286824])

reg_glmnet = glmnet(X, y, alpha = 1, lambda = 0.02,standardize = T)
coef(reg_glmnet)

4 x 1 sparse Matrix of class "dgCMatrix"
                    s0
(Intercept) -0.8960770
V1           .        
V2          -0.2474338
V3           1.0328682

from sklearn.linear_model import Lasso
Z = np.array([[1, 1, 2], [3, 4, 2], [6, 5, 2], [5, 5, 3]])
y = np.array([1, 0, 0, 1])
reg = Lasso(alpha =0.01, fit_intercept = True, normalize =False)
reg.fit(Z, y)
np.hstack((reg.intercept_, reg.coef_))

Out[96]: array([-0.88      ,  0.09384212, -0.36159299,  1.05958478])

reg_glmnet = glmnet(X, y, alpha = 1, lambda = 0.02,standardize = F)
coef(reg_glmnet)

4 x 1 sparse Matrix of class "dgCMatrix"
                     s0
(Intercept) -0.76000000
V1           0.04441697
V2          -0.29415542
V3           0.97623074

然而，如果我不想标准化变量并设置normalize=False和normalize=F，它们会给出完全不同的结果

Python:

from sklearn.linear_model import Lasso
X = np.array([[1, 1, 2], [3, 4, 2], [6, 5, 2], [5, 5, 3]])
y = np.array([1, 0, 0, 1])
reg = Lasso(alpha =0.01, fit_intercept = True, normalize =True)
reg.fit(X, y)
np.hstack((reg.intercept_, reg.coef_))

Out[95]: array([-0.89607695,  0.        , -0.24743375,  1.03286824])

reg_glmnet = glmnet(X, y, alpha = 1, lambda = 0.02,standardize = T)
coef(reg_glmnet)

4 x 1 sparse Matrix of class "dgCMatrix"
                    s0
(Intercept) -0.8960770
V1           .        
V2          -0.2474338
V3           1.0328682

from sklearn.linear_model import Lasso
Z = np.array([[1, 1, 2], [3, 4, 2], [6, 5, 2], [5, 5, 3]])
y = np.array([1, 0, 0, 1])
reg = Lasso(alpha =0.01, fit_intercept = True, normalize =False)
reg.fit(Z, y)
np.hstack((reg.intercept_, reg.coef_))

Out[96]: array([-0.88      ,  0.09384212, -0.36159299,  1.05958478])

reg_glmnet = glmnet(X, y, alpha = 1, lambda = 0.02,standardize = F)
coef(reg_glmnet)

4 x 1 sparse Matrix of class "dgCMatrix"
                     s0
(Intercept) -0.76000000
V1           0.04441697
V2          -0.29415542
V3           0.97623074

R:

from sklearn.linear_model import Lasso
X = np.array([[1, 1, 2], [3, 4, 2], [6, 5, 2], [5, 5, 3]])
y = np.array([1, 0, 0, 1])
reg = Lasso(alpha =0.01, fit_intercept = True, normalize =True)
reg.fit(X, y)
np.hstack((reg.intercept_, reg.coef_))

Out[95]: array([-0.89607695,  0.        , -0.24743375,  1.03286824])

reg_glmnet = glmnet(X, y, alpha = 1, lambda = 0.02,standardize = T)
coef(reg_glmnet)

4 x 1 sparse Matrix of class "dgCMatrix"
                    s0
(Intercept) -0.8960770
V1           .        
V2          -0.2474338
V3           1.0328682

from sklearn.linear_model import Lasso
Z = np.array([[1, 1, 2], [3, 4, 2], [6, 5, 2], [5, 5, 3]])
y = np.array([1, 0, 0, 1])
reg = Lasso(alpha =0.01, fit_intercept = True, normalize =False)
reg.fit(Z, y)
np.hstack((reg.intercept_, reg.coef_))

Out[96]: array([-0.88      ,  0.09384212, -0.36159299,  1.05958478])

reg_glmnet = glmnet(X, y, alpha = 1, lambda = 0.02,standardize = F)
coef(reg_glmnet)

4 x 1 sparse Matrix of class "dgCMatrix"
                     s0
(Intercept) -0.76000000
V1           0.04441697
V2          -0.29415542
V3           0.97623074

---

Python的Lasso中的“normalize”与R的glmnet中的“standarize”有什么区别？

目前，关于

normalize

参数，状态“如果您希望标准化，请在调用fit对

normalize=False

的估计器之前使用

StandardScaler

”

---

因此，显然，规范化和标准化与

sklearn.linear_model.Lasso

不同。阅读了

StandardScaler

文档后，我无法理解其中的区别，但提供的

normalize

参数说明暗示了这一点。

目前，关于

normalize

参数状态“如果您希望标准化，请在使用

normalize=False

调用估计器拟合之前使用

StandardScaler

”

显然，规范化和标准化与

sklearn.linear\u model.Lasso

不同。阅读了

StandardScaler

文档后，我无法理解其中的差异，但是

normalize

参数的描述暗示了存在差异的事实