sparse_ho.models.SparseLogreg

class sparse_ho.models.SparseLogreg(estimator=None)

Sparse Logistic Regression classifier.

The objective function is:

sum_1^n_samples log(1 + e^{-y_i x_i^T w}) + 1. / C * ||w||_1

Parameters

estimator: sklearn estimator

Estimator used to solve the optimization problem. Must follow 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 I have the feeling this is not used anywhere
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.