|Build Status| |Python 3.6+|
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:
$$ \minw \sum{i=1}^{n} \log(1 + \exp(-y_i x_i^\top w)) + \frac{\lambda}{2} \lVert w \rVert_2^2 $$
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:
.. code-block:: shell
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.:
.. code-block:: shell
$ 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.
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