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} 1/2n * ||y - Xw||^2 + \lambda * (l1ratio ||w||_1 + (1 - l1ratio) ||w||^2 / 2)
where n (or n_samples) stands for the number of samples, p (or n_features) stands for the number of features and
X = [x_1^\top, \dots, x_n^\top]^\top \in \mathbb{R}^{n \times p}
This benchmark can be run using the following commands:
$ 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.:
$ 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.