This repository contains an efficient python implementation of a SsNAL method to solve the elastic net problem -- see https://arxiv.org/abs/2006.03970
FILES DESCRIPTION:
ssnal_elastic_core:
function to run the SsNAL-EN algorithm for one fixed value of c_lam
ssnal_elastic_tune:
function to run the SsNAL-EN algorithm for a grid of c_lam and compute the tuning criteria for each of them.
auxiliary_functions.py
contains the auxiliary functions called by ssnal_elastic_core and ssnal_elastic_path, including proximal operator functions and conjugate functions.
expes/main_core.py:
main file to run ssnal_elastic_core on synthetic data
expes/main_path.py:
main file to run ssnal_elastic_path on synthetic data
expes/main_datasets.py:
main file to run ssnal_elastic_core on the real data described in the article and contained in the toy_data folder. The user has to select the data to analyze
expes/toy_data:
folder containing the used LIBSVM datasets (housing is loaded directly from a python library)
THE FOLLOWING PYTHON PACKAGES ARE REQUIRED:
- numpy
- sklearn
- scipy
- tqdm
You can install the package by running pip install -e . at the root of the repository, i.e. where the setup.py file is.