benchopt 1.2.0

Benchmark toolkit for optimization

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:

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

• Ordinary Least Squares (OLS)

• Non-Negative Least Squares (NNLS)

• LASSO: L1-regularized least squares

• L2-regularized logistic regression

• L1-regularized logistic regression

• L2-regularized Huber regression

• L1-regularized quantile regression

• Linear SVM for binary classification

• Linear ICA

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.

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