Skip to main content

Benchmark toolkit for optimization

Project description

Test Status Python 3.6+ codecov

BenchOpt is a benchmarking suite for optimization algorithms. It is built for simplicity, transparency, and reproducibility.

Benchopt is implemented in Python, and can run algorithms written in many programming languages (example). So far, Benchopt has been tested with Python, R, Julia and C/C++ (compiled binaries with a command line interface). Programs available via conda should be compatible.

BenchOpt is run through a command line interface as described in the API Documentation. Replicating an optimization benchmark should be as simple as doing:

conda create -n benchopt python
conda activate benchopt
pip install benchopt
git clone https://github.com/benchopt/benchmark_logreg_l2
benchopt install -e benchmark_logreg_l2 -s cd -s sklearn
benchopt run -e ./benchmark_logreg_l2 -s cd -s sklearn

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

https://benchopt.github.io/_images/sphx_glr_plot_run_benchmark_001.png

See the List of optimization problems available below.

Learn how to create a new benchmark using the benchmark template.

Install

The command line tool to run the benchmarks can be installed through pip. In order to allow benchopt to automatically install solvers dependencies, the install needs to be done in a conda environment.

conda create -n benchopt python
conda activate benchopt

To get the latest release, use:

pip install benchopt

To get the latest development version, use:

pip install -U -i https://test.pypi.org/simple/ benchopt

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

To get more details about the different options, run:

benchopt run -h

or read the API Documentation.

List of optimization problems available

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

benchopt-1.2.0.tar.gz (327.1 kB view hashes)

Uploaded source

Built Distribution

benchopt-1.2.0-py3-none-any.whl (114.2 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page