skglm.penalties.WeightedL1#

class skglm.penalties.WeightedL1(alpha, weights, positive=False)[source]#

Weighted L1 penalty.

__init__(alpha, weights, positive=False)[source]#

Methods

__init__(alpha, weights[, 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 of weighted L1 (weighted soft-thresholding).

subdiff_distance(w, grad, ws)

Compute distance of negative gradient to the subdifferential at w.

value(w)

Compute the weighted L1 penalty.