BenchOpt is a package to simplify and make more transparent and reproducible the comparisons of optimization algorithms. The Lasso consists in solving the following program:
\min_w \frac{1}{2} \|y - Xw\|^2_2 + \lambda \|w\|_1
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_lasso $ benchopt run ./benchmark_lasso
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_lasso -s sklearn -d boston --max-runs 10 --n-repetitions 10
Use benchopt run -h for more details about these options, or visit https://benchopt.github.io/api.html.
If you run into some errors when running the examples present in this Readme, try installing the development version of benchopt:
$ pip install -U git+https://github.com/benchopt/benchopt
If issues persist, you can also try running the benchmark in local mode with the -l option, e.g.:
$ benchopt run ./benchmark_lasso -l -s sklearn -d boston --max-runs 10 --n-repetitions 10
Note that in this case, only solvers which dependencies are installed in the current env will be run.