Benchopt is a package to simplify and make more transparent and reproducible the comparisons of optimization algorithms. The linear SVM consists in solving the following program:

\min_w C \sum_{i=1}^{n} max(1 - y_i x_i^\top w, 0) + \frac{1}{2} \sum_{j=1}^p w_j^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_linear_svm_binary_classif_no_intercept
$ benchopt run ./benchmark_linear_svm_binary_classif_no_intercept

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_linear_svm_binary_classif_no_intercept -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/cli.html.