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)[source]

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)[source]

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