sparse_ho.models.WeightedLasso

class sparse_ho.models.WeightedLasso(estimator=None)

Linear Model trained with weighted L1 regularizer (aka weighted Lasso).

The optimization objective for weighted Lasso is:

..math:

||y - Xw||^2_2 / (2 * n_samples) + \sum_i^{n_features} \alpha_i |wi|

Parameters

estimator: instance of ``sklearn.base.BaseEstimator``

An estimator that follows the scikit-learn API.

__init__(estimator=None)

Methods

__init__([estimator])
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_residual_norm(Xs, ys, n_samples, ...)
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.