sparse_ho.models.ElasticNet

class sparse_ho.models.ElasticNet(estimator=None)

Sparse ho ElasticNet model (inner problem).

Parameters
estimator: sklearn estimator

Estimator used to solve the optimization problem. Must follow the scikit-learn API.

__init__(estimator=None)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([estimator])

Initialize self.

generalized_supp(X, v, log_alpha)

Generalized support of iterate.

get_L(X)

Compute Lipschitz constant of datafit.

get_beta(X, y, mask, dense)

Return primal iterate.

get_full_jac_v(mask, jac_v, n_features)

TODO

get_jac_obj(Xs, ys, n_samples, beta, dbeta, …)

get_jac_v(X, y, mask, dense, jac, v)

Compute hypergradient.

get_mask_jac_v(mask, jac_v)

TODO

get_mat_vec(X, y, mask, dense, log_alpha)

Returns a LinearOperator computing the matrix vector product with the Hessian of datafit.

proj_hyperparam(X, y, log_alpha)

Project hyperparameter on an admissible range of values.

reduce_X(X, mask)

Reduce design matrix to generalized support.

reduce_y(y, mask)

Reduce observation vector to generalized support.

sign(x, log_alpha)

Get sign of iterate.