skglm.experimental.IterativeReweightedL1

class skglm.experimental.IterativeReweightedL1(datafit=<skglm.datafits.single_task.Quadratic object>, penalty=<skglm.penalties.separable.L0_5 object>, solver=None, n_reweights=5)

Reweighted L1-norm estimator.

This estimator solves a non-convex problems by iteratively solving convex surrogates involving weighted L1 norms.

Parameters:

datafitinstance of BaseDatafit, optional

Datafit. If None, datafit is initialized as a Quadratic datafit. datafit is replaced by a JIT-compiled instance when calling fit.

penaltyinstance of BasePenalty, optional

Penalty. If None, penalty is initialized as a L0_5 penalty. penalty is replaced by a JIT-compiled instance when calling fit.

solverinstance of BaseSolver, optional

Solver. If None, solver is initialized as an AndersonCD solver.

n_reweightsint, optional

Number of reweighting performed (convex surrogates solved).

References

[1]

Candès et al. (2007), Enhancing sparsity by reweighted l1 minimization https://web.stanford.edu/~boyd/papers/pdf/rwl1.pdf

Attributes:

coef_array, shape (n_features,)

Parameter vector (`w` in the cost function formula).

loss_history_list

Objective history after every reweighting.

__init__(datafit=<skglm.datafits.single_task.Quadratic object>, penalty=<skglm.penalties.separable.L0_5 object>, solver=None, n_reweights=5)

Methods

__init__([datafit, penalty, solver, n_reweights])
fit(X, y)	Fit the model according to the given training data.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters of the estimators including the datafit's and penalty's.
predict(X)	Predict target values for samples in X.
set_params(**params)	Set the parameters of this estimator.