benchopt 1.1.0

Benchmark toolkit for optimization

BenchOpt is a package to simplify, make more transparent and more reproducible the comparisons of optimization algorithms.

BenchOpt is written in Python but it is available with many programming languages. So far it has been tested with Python, R, Julia and compiled binaries written in C/C++ available via a terminal command. If it can be installed via conda it should just work!

BenchOpt is used through a command line as described in the API Documentation. Ultimately running and replicating an optimization benchmark should be as simple as doing:

$ git clone https://github.com/benchopt/benchmark_logreg_l2
$ benchopt run --env ./benchmark_logreg_l2

Running this command will give you a benchmark plot on l2-regularized logistic regression:

To discover which benchmarks are presently available look for benchmark_* repositories on GitHub, such as for l1-regularized logistic regression.

Learn how to write a benchmark on our documentation.

Install

This package can be installed through pip. To get the last release, use:

$ pip install benchopt

And to get the latest development version, you can use:

$ pip install -U https://github.com/benchopt/benchOpt/archive/master.zip

This will install the command line tool to run the benchmark. Then, existing benchmarks can be retrieved from git or created locally. For instance, the benchmark for Lasso can be retrieved with:

$ git clone https://github.com/benchopt/benchmark_lasso

Command line usage

To run the Lasso benchmark on all datasets and with all solvers, run:

$ benchopt run --env ./benchmark_lasso

Use

$ benchopt run -h

to get more details about the different options or read the API Documentation.

List of optimization problems available

• ols: ordinary least-squares.

• nnls: non-negative least-squares.

• lasso: l1-regularized least-squares.

• logreg_l2: l2-regularized logistic regression.

• logreg_l1: l1-regularized logistic regression.

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New BSD License

Copyright (c) 2019–2020 The benchOpt developers. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the Scikit-learn Developers nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.