Benchopt is a package to simplify and make more transparent and reproducible the comparisons of optimization algorithms. The L2-regularized Logistic Regression consists in solving the following program:

$$ \min_w \sum_{i=1}^{n} \log(1 + \exp(-y_i x_i^\top w)) + \frac{\lambda}{2} \lVert w \rVert_2^2 $$

where $n$ (or n_samples) stands for the number of samples, $p$ (or n_features) stands for the number of features and

$$ y \in \mathbb{R}^n, X = [x_1^\top, \dots, x_n^\top]^\top \in \mathbb{R}^{n \times p} $$

This benchmark can be run using the following commands:

pip install -U benchopt
git clone https://github.com/benchopt/benchmark_logreg_l2
benchopt run ./benchmark_logreg_l2

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_logreg_l2 -s sklearn -d simulated --max-runs 10 --n-repetitions 10

Use benchopt run -h for more details about these options, or visit https://benchopt.github.io/api.html.