https://github.com/benchopt/benchmark_elastic_net
Benchmark repository for the elastic net problem
https://github.com/benchopt/benchmark_elastic_net
Last synced: about 1 year ago
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Benchmark repository for the elastic net problem
- Host: GitHub
- URL: https://github.com/benchopt/benchmark_elastic_net
- Owner: benchopt
- Created: 2022-02-09T13:07:25.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-06-28T12:48:31.000Z (almost 3 years ago)
- Last Synced: 2023-06-28T13:41:12.668Z (almost 3 years ago)
- Language: Python
- Homepage:
- Size: 12.7 KB
- Stars: 3
- Watchers: 3
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.rst
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README
Elastic Net Benchmark
=====================
|Build Status| |Python 3.6+|
Benchopt is a package to simplify and make more transparent and
reproducible the comparisons of optimization algorithms.
This benchmark is dedicated to elastic net regression:
$$ \\min_w \\frac{1}{2n} \\Vert y - Xw \\Vert^2 + \\lambda \\ (\\rho \\Vert w \\Vert_1 + \\frac{1 - \\rho}{2} \\Vert w \\Vert^2)$$
where $n$ (or ``n_samples``) stands for the number of samples, $p$ (or ``n_features``) stands for the number of features
, $\\rho \\in (0, 1]$ is the ``l1_ratio`` and
$$y \\in \\mathbb{R}^n , \\ X = [x_1^\\top, \\dots, x_n^\\top]^\\top \\in \\mathbb{R}^{n \\times p}$$
Install
--------
This benchmark can be run using the following commands:
.. code-block::
$ pip install -U benchopt
$ git clone https://github.com/benchopt/benchmark_elastic_net
$ benchopt run benchmark_elastic_net
Apart from the problem, options can be passed to ``benchopt run``, to restrict the benchmarks to some solvers or datasets, e.g.:
.. code-block::
$ benchopt run benchmark_elastic_net -s solver1 -d dataset2 --max-runs 10 --n-repetitions 10
Use ``benchopt run -h`` for more details about these options, or visit https://benchopt.github.io/api.html.
.. |Build Status| image:: https://github.com/benchopt/benchmark_elastic_net/workflows/Tests/badge.svg
:target: https://github.com/benchopt/benchmark_elastic_net/actions
.. |Python 3.6+| image:: https://img.shields.io/badge/python-3.6%2B-blue
:target: https://www.python.org/downloads/release/python-360/