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benchopt-1.3.1
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مشاهده بیشترتوضیحات
Benchmark toolkit for optimization
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | benchopt-1.3.1 |
| نام | benchopt |
| نسخه کتابخانه | 1.3.1 |
| نگهدارنده | ['Thomas Moreau'] |
| ایمیل نگهدارنده | ['thomas.moreau@inria.fr'] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/benchopt/ |
| مجوز | BSD (3-clause) |
Benchmark repository for optimization
=====================================
|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 <https://benchopt.github.io/auto_examples/plot_run_benchmark_python_R.html>`_).
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.
BenchOpt is run through a command line interface as described
in the `API Documentation <https://benchopt.github.io/api.html>`_.
Replicating an optimization benchmark should
be **as simple as doing**:
.. code-block::
conda create -n benchopt python
conda activate benchopt
pip install benchopt
git clone https://github.com/benchopt/benchmark_logreg_l2
cd benchmark_logreg_l2
benchopt install -e . -s lightning -s sklearn
benchopt run -e . --config ./example_config.yml
Running this command will give you a benchmark plot on l2-regularized
logistic regression:
.. figure:: https://benchopt.github.io/_images/sphx_glr_plot_run_benchmark_001.png
:target: how.html
:align: center
:scale: 80%
See the `Available optimization problems`_ below.
Learn how to `create a new benchmark <https://benchopt.github.io/how.html>`_
using the `benchmark template <https://github.com/benchopt/template_benchmark>`_.
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.
.. code-block::
conda create -n benchopt python
conda activate benchopt
To get the **latest release**, use:
.. code-block::
pip install benchopt
To get the **latest development version**, use:
.. code-block::
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:
.. code-block::
git clone https://github.com/benchopt/benchmark_lasso
Command line interface
----------------------
The preferred way to run the benchmarks is through the command line interface.
To run the Lasso benchmark on all datasets and with all solvers, run:
.. code-block::
benchopt run --env ./benchmark_lasso
To get more details about the different options, run:
.. code-block::
benchopt run -h
or read the `CLI documentation <https://benchopt.github.io/cli.html>`_.
Benchopt also provides a Python API described in the
`API documentation <https://benchopt.github.io/api.html>`_.
Available optimization problems
-------------------------------
.. list-table::
:widths: 70 15 15
:header-rows: 1
* - Problem
- Results
- Build Status
* - `Ordinary Least Squares (OLS) <https://github.com/benchopt/benchmark_ols>`_
- `Results <https://benchopt.github.io/results/benchmark_ols.html>`__
- |Build Status OLS|
* - `Non-Negative Least Squares (NNLS) <https://github.com/benchopt/benchmark_nnls>`_
- `Results <https://benchopt.github.io/results/benchmark_nnls.html>`__
- |Build Status NNLS|
* - `LASSO: L1-Regularized Least Squares <https://github.com/benchopt/benchmark_lasso>`_
- `Results <https://benchopt.github.io/results/benchmark_lasso.html>`__
- |Build Status Lasso|
* - `LASSO Path <https://github.com/jolars/benchmark_lasso_path>`_
- `Results <https://benchopt.github.io/results/benchmark_lasso_path.html>`__
- |Build Status Lasso Path|
* - `Elastic Net <https://github.com/benchopt/benchmark_elastic_net>`_
-
- |Build Status ElasticNet|
* - `MCP <https://github.com/benchopt/benchmark_mcp>`_
- `Results <https://benchopt.github.io/results/benchmark_mcp.html>`__
- |Build Status MCP|
* - `L2-Regularized Logistic Regression <https://github.com/benchopt/benchmark_logreg_l2>`_
- `Results <https://benchopt.github.io/results/benchmark_logreg_l2.html>`__
- |Build Status LogRegL2|
* - `L1-Regularized Logistic Regression <https://github.com/benchopt/benchmark_logreg_l1>`_
- `Results <https://benchopt.github.io/results/benchmark_logreg_l1.html>`__
- |Build Status LogRegL1|
* - `L2-regularized Huber regression <https://github.com/benchopt/benchmark_huber_l2>`_
-
- |Build Status HuberL2|
* - `L1-Regularized Quantile Regression <https://github.com/benchopt/benchmark_quantile_regression>`_
- `Results <https://benchopt.github.io/results/benchmark_quantile_regression.html>`__
- |Build Status QuantileRegL1|
* - `Linear SVM for Binary Classification <https://github.com/benchopt/benchmark_linear_svm_binary_classif_no_intercept>`_
-
- |Build Status LinearSVM|
* - `Linear ICA <https://github.com/benchopt/benchmark_linear_ica>`_
-
- |Build Status LinearICA|
* - `Approximate Joint Diagonalization (AJD) <https://github.com/benchopt/benchmark_jointdiag>`_
-
- |Build Status JointDiag|
* - `1D Total Variation Denoising <https://github.com/benchopt/benchmark_tv_1d>`_
-
- |Build Status TV1D|
* - `2D Total Variation Denoising <https://github.com/benchopt/benchmark_tv_2d>`_
-
- |Build Status TV2D|
* - `ResNet Classification <https://github.com/benchopt/benchmark_resnet_classif>`_
- `Results <https://benchopt.github.io/results/benchmark_resnet_classif.html>`__
- |Build Status ResNetClassif|
Citing Benchopt
---------------
If you use ``Benchopt`` in a scientific publication, please cite the following paper
.. code-block:: bibtex
@article{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},
url = {https://arxiv.org/abs/2206.13424}
}
.. |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
.. |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
BSD 3-Clause License
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.
b. 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.
c. Neither the name of the Benchopt 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
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DAMAGE.
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | scipy |
| - | pandas |
| - | matplotlib |
| >=8.0 | click |
| - | joblib |
| - | pygithub |
| - | mako |
| - | psutil |
| >=4.12 | plotly |
| - | pyyaml |
| - | line-profiler |
| - | pyarrow |
| - | numpydoc |
| ==4.5.0 | sphinx |
| - | sphinx-bootstrap-theme |
| - | sphinx-click |
| - | sphinx-gallery |
| - | sphinx-prompt |
| - | pillow |
| - | scikit-learn |
| - | submitit |
| - | rich |
| - | pytest |
| - | pytest-cov |
| - | pytest-timeout |
| - | coverage |
نحوه نصب
نصب پکیج whl benchopt-1.3.1:
pip install benchopt-1.3.1.whl
نصب پکیج tar.gz benchopt-1.3.1:
pip install benchopt-1.3.1.tar.gz