Metadata-Version: 2.1
Name: benchopt
Version: 1.1.0
Summary: Benchmark toolkit for optimization
Home-page: UNKNOWN
Maintainer: Thomas Moreau
Maintainer-email: thomas.moreau@inria.fr
License: BSD (3-clause)
Download-URL: https://github.com/benchopt/benchopt.git
Description: Benchmark repository for optimization
        =====================================
        
        |Test Status| |Python 3.6+| |codecov|
        
        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 <https://benchopt.github.io/auto_examples/plot_run_benchmark_python_R_julia.html>`_.
        So far it has been tested with `Python <https://www.python.org/>`_,
        `R <https://www.r-project.org/>`_, `Julia <https://julialang.org/>`_
        and compiled binaries written in C/C++ available via a terminal
        command. If it can be installed via
        `conda <https://docs.conda.io/en/latest/>`_ it should just work!
        
        BenchOpt is used through a command line as described
        in `the API Documentation <https://benchopt.github.io/api.html>`_.
        Ultimately running and replicating an optimization benchmark should
        be **as simple as doing**:
        
        .. code-block::
        
            $ 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:
        
        .. figure:: https://benchopt.github.io/_images/sphx_glr_plot_run_benchmark_001.png
           :target: how.html
           :align: center
           :scale: 80%
        
        To discover which benchmarks are presently available look
        for `benchmark_* repositories on GitHub <https://github.com/benchopt/>`_,
        such as for
        `l1-regularized logistic regression <https://github.com/benchopt/benchmark_logreg_l1>`_.
        
        
        Learn how to `write a benchmark on our documentation <https://benchopt.github.io/how.html>`_.
        
        Install
        --------
        
        This package can be installed through `pip`. To get the **last release**, use:
        
        .. code-block::
        
            $ pip install benchopt
        
        And to get the **latest development version**, you can use:
        
        .. code-block::
        
            $ 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:
        
        .. code-block::
        
            $ git clone https://github.com/benchopt/benchmark_lasso
        
        
        Command line usage
        ------------------
        
        To run the Lasso benchmark on all datasets and with all solvers, run:
        
        .. code-block::
        
            $ benchopt run --env ./benchmark_lasso
        
        Use
        
        .. code-block::
        
            $ benchopt run -h
        
        to get more details about the different options or read the
        `API Documentation <https://benchopt.github.io/api.html>`_.
        
        
        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.
        
        [![test]()]()
        
        .. |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
        
        .. _`ols`: https://github.com/benchopt/benchmark_ols
        .. _`nnls`: https://github.com/benchopt/benchmark_nnls
        .. _`lasso`: https://github.com/benchopt/benchmark_lasso
        .. _`logreg_l1`: https://github.com/benchopt/benchmark_logreg_l1
        .. _`logreg_l2`: https://github.com/benchopt/benchmark_logreg_l2
        
        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:
        
          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 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.
        
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: R
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Classifier: Topic :: Software Development :: Libraries
Description-Content-Type: text/x-rst
Provides-Extra: test
Provides-Extra: doc
