secure_learning.models.secure_elastic_nets module

Implementation of ElasticNets regression model.

class secure_learning.models.secure_elastic_nets.ElasticNets(solver_type=SolverTypes.GD, alpha1=1, alpha2=1)

Bases: Linear

Solver for Elastic Nets regression. Optimizes a model with objective function: $$frac{1}{2{n}_{textrm{samples}}} times ||y - X_times_w||^2_2 + alpha_1 ||w||_1 + frac{alpha_2 ||w||^2_2}{2}$$

__init__(solver_type=SolverTypes.GD, alpha1=1, alpha2=1)

Constructor method.

Parameters:

• solver_type (SolverTypes) – Solver type to use (e.g. Gradient Descent aka GD)

• alpha1 (float) – Regularisation parameter for L2

• alpha2 (float) – Regularisation parameter for L2

name = 'Elastic nets regression'