MF2
MF2: A Collection of Multi-Fidelity Benchmark Functions in Python - Published in JOSS (2020)
Science Score: 100.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓DOI references
Found 9 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
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1 of 6 committers (16.7%) from academic institutions -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Repository
Collection of Multi-Fidelity benchmark functions
Basic Info
- Host: GitHub
- Owner: sjvrijn
- License: gpl-3.0
- Language: Python
- Default Branch: main
- Homepage: https://mf2.readthedocs.io/
- Size: 1.1 MB
Statistics
- Stars: 28
- Watchers: 2
- Forks: 8
- Open Issues: 8
- Releases: 8
Topics
Metadata Files
README.md
MF2: Multi-Fidelity-Functions
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Introduction
The mf2 package provides consistent, efficient and tested Python
implementations of a variety of multi-fidelity benchmark functions. The goal is
to simplify life for numerical optimization researchers by saving time otherwise
spent reimplementing and debugging the same common functions, and enabling
direct comparisons with other work using the same definitions, improving
reproducibility in general.
A multi-fidelity function usually reprensents an objective which should be optimized. The term 'multi-fidelity' refers to the fact that multiple versions of the objective function exist, which differ in the accuracy to describe the real objective. A typical real-world example would be the aerodynamic efficiency of an airfoil, e.g., its drag value for a given lift value. The different fidelity levels are given by the accuracy of the evaluation method used to estimate the efficiency. Lower-fidelity versions of the objective function refer to less accurate, but simpler approximations of the objective, such as computational fluid dynamic simulations on rather coarse meshes, whereas higher fidelity levels refer to more accurate but also much more demanding evaluations such as prototype tests in wind tunnels. The hope of multi-fildelity optimization approaches is that many of the not-so-accurate but simple low-fidelity evaluations can be used to achieve improved results on the realistic high-fidelity version of the objective where only very few evaluations can be performed.
The only dependency of the mf2 package is the numpy package.
Documentation is available at mf2.readthedocs.io
Installation
The recommended way to install mf2 in your (virtual) environment is with
Python's pip:
pip install mf2
or alternatively using conda:
conda install -c conda-forge mf2
For the latest version, you can install directly from source:
pip install https://github.com/sjvrijn/mf2/archive/main.zip
To work in your own version locally, it is best to clone the repository first,
and additionally create an editable install that includes the dev-requirements:
git clone https://github.com/sjvrijn/mf2.git
cd mf2
pip install -e ".[dev]"
Example Usage
```python import mf2 import numpy as np
set numpy random seed for reproducibility
np.random.seed(42)
generate 5 random samples in 2D as matrix
X = np.random.random((5, 2))
print high fidelity function values
print(mf2.branin.high(X))
Out: array([36.78994906 34.3332972 50.48149005 43.0569396 35.5268224 ])
print low fidelity function values
print(mf2.branin.low(X))
Out: array([-5.8762639 -6.66852889 3.84944507 -1.56314141 -6.23242223])
```
For more usage examples, please refer to the full documentation on readthedocs.
Contributing
Contributions to this project such as bug reports or benchmark function
suggestions are more than welcome! Please refer to
CONTRIBUTING.md for more details.
Contact
The Gitter channel is the preferred way to get in touch for any other questions, comments or discussions about this package.
Citation
Was this package useful to you? Great! If this leads to a publication, we'd appreciate it if you would cite our JOSS paper:
@article{vanRijn2020,
doi = {10.21105/joss.02049},
url = {https://doi.org/10.21105/joss.02049},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {52},
pages = {2049},
author = {Sander van Rijn and Sebastian Schmitt},
title = {MF2: A Collection of Multi-Fidelity Benchmark Functions in Python},
journal = {Journal of Open Source Software}
}
Owner
- Name: Sander van Rijn
- Login: sjvrijn
- Kind: user
- Location: Leiden, Netherlands
- Company: @NLeSC
- Website: http://www.svrijn.nl
- Twitter: sjvrijn
- Repositories: 34
- Profile: https://github.com/sjvrijn
Research Software Engineer at Netherlands eScience Center | Computer Science PhD Candidate at LIACS
JOSS Publication
MF2: A Collection of Multi-Fidelity Benchmark Functions in Python
Authors
Leiden University, The Netherlands
Honda Research Institute Europe, Germany
Editor
Melissa Weber Mendonça
Tags
optimization benchmarksCitation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "van Rijn"
given-names: "Sander"
affiliation: "Leiden University"
orcid: "https://orcid.org/0000-0001-6159-041X"
- family-names: "Schmitt"
given-names: "Sebastian"
affiliation: "Honda Research Institute Europe"
orcid: "https://orcid.org/0000-0001-7130-5483"
title: "MF2: Multi-Fidelity-Functions"
version: 2021.10.0
doi: 10.5281/zenodo.3610282
date-released: 2021-10-22
url: "https://github.com/sjvrijn/mf2"
license: GPL-3.0
preferred-citation:
type: article
authors:
- family-names: "van Rijn"
given-names: "Sander"
affiliation: "Leiden University"
orcid: "https://orcid.org/0000-0001-6159-041X"
- family-names: "Schmitt"
given-names: "Sebastian"
affiliation: "Honda Research Institute Europe"
orcid: "https://orcid.org/0000-0001-7130-5483"
doi: "10.21105/joss.02049"
journal: "Journal of Open Source Software"
publisher: "The Open Journal"
title: "MF2: A Collection of Multi-Fidelity Benchmark Functions in Python"
pages: 2049
number: 52
volume: 5
year: 2020
GitHub Events
Total
- Watch event: 3
- Issue comment event: 1
- Push event: 8
- Pull request review event: 1
- Pull request event: 1
- Fork event: 1
Last Year
- Watch event: 3
- Issue comment event: 1
- Push event: 8
- Pull request review event: 1
- Pull request event: 1
- Fork event: 1
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Sander van Rijn | s****3@g****m | 342 |
| Izaak Beekman | z****n@g****m | 4 |
| xiaomei | 1****5 | 1 |
| Sourcery AI | b****t@s****i | 1 |
| Arfon Smith | a****n | 1 |
| S.J. van Rijn | s****2@u****l | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 22
- Total pull requests: 39
- Average time to close issues: about 2 months
- Average time to close pull requests: 5 days
- Total issue authors: 2
- Total pull request authors: 5
- Average comments per issue: 0.36
- Average comments per pull request: 0.54
- Merged pull requests: 37
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 1.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- sjvrijn (20)
- torressa (2)
Pull Request Authors
- sjvrijn (34)
- xm2325 (2)
- zbeekman (2)
- arfon (1)
- sourcery-ai-bot (1)
Top Labels
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Packages
- Total packages: 2
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Total downloads:
- pypi 105 last-month
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Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 1
(may contain duplicates) - Total versions: 15
- Total maintainers: 1
pypi.org: mf2
A collection of analytical benchmark functions in multiple fidelities
- Homepage: https://github.com/sjvrijn/mf2
- Documentation: https://mf2.readthedocs.io/
- License: GNU General Public License v3 (GPLv3)
-
Latest release: 2022.6.0
published over 3 years ago
Rankings
Maintainers (1)
conda-forge.org: mf2
- Homepage: https://github.com/sjvrijn/mf2
- License: GPL-3.0-or-later
-
Latest release: 2022.6.0
published over 3 years ago
Rankings
Dependencies
- actions/checkout v2 composite
- actions/setup-python v1 composite
- pypa/gh-action-pypi-publish master composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- numpy *