Readme
.. image:: https://raw.githubusercontent.com/benchopt/communication_materials/main/posters/images/logo_benchopt.png :width: 350 :align: center
—Making your optimization benchmarks simple and open—
|Test Status| |codecov| |Python 3.6+| |install-per-months| |discord| |SWH|
Benchopt is a benchmarking suite for optimization algorithms.
It is built for simplicity, transparency, and reproducibility.
It is implemented in Python but can run algorithms written in many programming languages.
So far, benchopt has been tested with Python <https://www.python.org/>,
R <https://www.r-project.org/>, Julia <https://julialang.org/>_
and C/C++ <https://isocpp.org/>_ (compiled binaries with a command line interface).
Programs available via conda <https://docs.conda.io/en/latest/>_ should be compatible as well.
See for instance an example of usage <https://benchopt.github.io/auto_examples/plot_run_benchmark_python_R.html>_ with R.
It is recommended to use benchopt within a conda environment to fully-benefit
from benchopt Command Line Interface (CLI).
To install benchopt, start by creating a new conda environment and then activate it
.. code-block:: bash
conda create -n benchopt python
conda activate benchopt
Then run the following command to install the latest release of benchopt
.. code-block:: bash
pip install -U benchopt
It is also possible to use the latest development version. To do so, run instead
.. code-block:: bash
pip install --pre benchopt -U -i https://test.pypi.org/simple
After installing benchopt, you can
Using an existing benchmark ^^^^^^^^^^^^^^^^^^^^^^^^^^^
Replicating an existing benchmark is simple.
Here is how to do so for the L2-logistic Regression benchmark <https://github.com/benchopt/benchmark_logreg_l2>_.
cd to it.. code-block:: bash
git clone https://github.com/benchopt/benchmark_logreg_l2 cd benchmark_logreg_l2
benchopt.. code-block:: bash
benchopt install . -s lightning -s sklearn
.. code-block:: bash
benchopt run . --config ./example_config.yml
.. figure:: https://benchopt.github.io/_images/sphx_glr_plot_run_benchmark_001.png :target: how.html :align: center :scale: 40%
These steps illustrate how to reproduce the L2-logistic Regression benchmark <https://github.com/benchopt/benchmark_logreg_l2>.
Find the complete list of the Available benchmarks.
Also, refer to the documentation <https://benchopt.github.io/>_ to learn more about benchopt CLI and its features.
You can also easily extend this benchmark by adding a dataset, solver or metric.
Learn that and more in the Benchmark workflow <https://benchopt.github.io/benchmark_workflow/index.html>_.
Creating a benchmark ^^^^^^^^^^^^^^^^^^^^
The section Write a benchmark <https://benchopt.github.io/benchmark_workflow/write_benchmark.html>_ of the documentation provides a tutorial
for creating a benchmark. The benchopt community also maintains
a template benchmark <https://github.com/benchopt/template_benchmark>_ to quickly and easily start a new benchmark.
Join benchopt discord server <https://discord.gg/EA2HGQb7nv>_ and get in touch with the community!
Feel free to drop us a message to get help with running/constructing benchmarks
or (why not) discuss new features to be added and future development directions that benchopt should take.
Benchopt is a continuous effort to make reproducible and transparent optimization benchmarks.
Join us in this endeavor! If you use benchopt in a scientific publication, please cite
.. code-block:: bibtex
@inproceedings{benchopt, author = {Moreau, Thomas and Massias, Mathurin and Gramfort, Alexandre and Ablin, Pierre and Bannier, Pierre-Antoine and Charlier, Benjamin and Dagréou, Mathieu and Dupré la Tour, Tom and Durif, Ghislain and F. Dantas, Cassio and Klopfenstein, Quentin and Larsson, Johan and Lai, En and Lefort, Tanguy and Malézieux, Benoit and Moufad, Badr and T. Nguyen, Binh and Rakotomamonjy, Alain and Ramzi, Zaccharie and Salmon, Joseph and Vaiter, Samuel}, title = {Benchopt: Reproducible, efficient and collaborative optimization benchmarks}, year = {2022}, booktitle = {NeurIPS}, url = {https://arxiv.org/abs/2206.13424} }
.. list-table:: :widths: 70 15 15 :header-rows: 1
Ordinary Least Squares (OLS) <https://github.com/benchopt/benchmark_ols>_Results <https://benchopt.github.io/results/benchmark_ols.html>__Non-Negative Least Squares (NNLS) <https://github.com/benchopt/benchmark_nnls>_Results <https://benchopt.github.io/results/benchmark_nnls.html>__LASSO: L1-Regularized Least Squares <https://github.com/benchopt/benchmark_lasso>_Results <https://benchopt.github.io/results/benchmark_lasso.html>__LASSO Path <https://github.com/jolars/benchmark_lasso_path>_Results <https://benchopt.github.io/results/benchmark_lasso_path.html>__Elastic Net <https://github.com/benchopt/benchmark_elastic_net>_MCP <https://github.com/benchopt/benchmark_mcp>_Results <https://benchopt.github.io/results/benchmark_mcp.html>__L2-Regularized Logistic Regression <https://github.com/benchopt/benchmark_logreg_l2>_Results <https://benchopt.github.io/results/benchmark_logreg_l2.html>__L1-Regularized Logistic Regression <https://github.com/benchopt/benchmark_logreg_l1>_Results <https://benchopt.github.io/results/benchmark_logreg_l1.html>__L2-regularized Huber regression <https://github.com/benchopt/benchmark_huber_l2>_L1-Regularized Quantile Regression <https://github.com/benchopt/benchmark_quantile_regression>_Results <https://benchopt.github.io/results/benchmark_quantile_regression.html>__Linear SVM for Binary Classification <https://github.com/benchopt/benchmark_linear_svm_binary_classif_no_intercept>_Linear ICA <https://github.com/benchopt/benchmark_linear_ica>_Approximate Joint Diagonalization (AJD) <https://github.com/benchopt/benchmark_jointdiag>_1D Total Variation Denoising <https://github.com/benchopt/benchmark_tv_1d>_2D Total Variation Denoising <https://github.com/benchopt/benchmark_tv_2d>_ResNet Classification <https://github.com/benchopt/benchmark_resnet_classif>_Results <https://benchopt.github.io/results/benchmark_resnet_classif.html>__Bilevel Optimization <https://github.com/benchopt/benchmark_bilevel>_Results <https://benchopt.github.io/results/benchmark_bilevel.html>__.. |Test Status| image:: https://github.com/benchopt/benchopt/actions/workflows/test.yml/badge.svg :target: https://github.com/benchopt/benchopt/actions/workflows/test.yml .. |Python 3.6+| image:: https://img.shields.io/badge/python-3.6%2B-blue :target: https://www.python.org/downloads/release/python-360/ .. |codecov| image:: https://codecov.io/gh/benchopt/benchopt/branch/master/graph/badge.svg :target: https://codecov.io/gh/benchopt/benchopt .. |SWH| image:: https://archive.softwareheritage.org/badge/origin/https://github.com/benchopt/benchopt/ :target: https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/benchopt/benchopt .. |discord| image:: https://dcbadge.vercel.app/api/server/EA2HGQb7nv?style=flat :target: https://discord.gg/EA2HGQb7nv .. |install-per-months| image:: https://static.pepy.tech/badge/benchopt/month :target: https://pepy.tech/project/benchopt
.. |Build Status OLS| image:: https://github.com/benchopt/benchmark_ols/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_ols/actions .. |Build Status NNLS| image:: https://github.com/benchopt/benchmark_nnls/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_nnls/actions .. |Build Status Lasso| image:: https://github.com/benchopt/benchmark_lasso/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_lasso/actions .. |Build Status Lasso Path| image:: https://github.com/jolars/benchmark_lasso_path/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_lasso_path/actions .. |Build Status ElasticNet| image:: https://github.com/benchopt/benchmark_elastic_net/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_elastic_net/actions .. |Build Status MCP| image:: https://github.com/benchopt/benchmark_mcp/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_mcp/actions .. |Build Status LogRegL2| image:: https://github.com/benchopt/benchmark_logreg_l2/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_logreg_l2/actions .. |Build Status LogRegL1| image:: https://github.com/benchopt/benchmark_logreg_l1/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_logreg_l1/actions .. |Build Status HuberL2| image:: https://github.com/benchopt/benchmark_huber_l2/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_huber_l2/actions .. |Build Status QuantileRegL1| image:: https://github.com/benchopt/benchmark_quantile_regression/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_quantile_regression/actions .. |Build Status LinearSVM| image:: https://github.com/benchopt/benchmark_linear_svm_binary_classif_no_intercept/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_linear_svm_binary_classif_no_intercept/actions .. |Build Status LinearICA| image:: https://github.com/benchopt/benchmark_linear_ica/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_linear_ica/actions .. |Build Status JointDiag| image:: https://github.com/benchopt/benchmark_jointdiag/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_jointdiag/actions .. |Build Status TV1D| image:: https://github.com/benchopt/benchmark_tv_1d/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_tv_1d/actions .. |Build Status TV2D| image:: https://github.com/benchopt/benchmark_tv_2d/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_tv_2d/actions .. |Build Status ResNetClassif| image:: https://github.com/benchopt/benchmark_resnet_classif/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_resnet_classif/actions .. |Build Status Bilevel| image:: https://github.com/benchopt/benchmark_bilevel/workflows/Tests/badge.svg :target: https://github.com/benchopt/benchmark_bilevel/actions
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Copyright (c) 2019–2022 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:
a. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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FAQs
Benchmark toolkit for optimization
We found that benchopt demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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