skglm.penalties.L1_plus_L2

class skglm.penalties.L1_plus_L2(alpha, l1_ratio, positive=False)

`ell_1 + ell_2` penalty (aka ElasticNet penalty).

__init__(alpha, l1_ratio, positive=False)

Methods

__init__(alpha, l1_ratio[, positive])
alpha_max(gradient0)	Return penalization value for which 0 is solution.
generalized_support(w)	Return a mask with non-zero coefficients.
get_spec()	Specify the numba types of the class attributes.
is_penalized(n_features)	Return a binary mask with the penalized features.
params_to_dict()	Get the parameters to initialize an instance of the class.
prox_1d(value, stepsize, j)	Compute the proximal operator (scaled soft-thresholding).
subdiff_distance(w, grad, ws)	Compute distance of negative gradient to the subdifferential at w.
value(w)	Compute the L1 + L2 penalty value.