spotdesirability
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About
spotdesirability is a Python implementation of the R package desirability, which provides S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
Installation
Installation can be done with pip:
pip install spotdesirability
or uv:
uv install spotdesirability
Alternatively, you can clone the repository and install it manually.
Documentation
The documentation is available at: https://sequential-parameter-optimization.github.io/spotdesirability/
Citation
@misc{bartz25a,
title={Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions},
author={Thomas Bartz-Beielstein},
year={2025},
eprint={2503.23595},
archivePrefix={arXiv},
primaryClass={math.OC},
url={https://arxiv.org/abs/2503.23595},
}
References
Desirability functions
- Derringer, G., and Suich, R. Simultaneous optimization of several response variables. Journal of Quality Technology 12 (1980), 214–219.
The R desirability package
- The
Rdesirabilitypackage is maintained and developed by Max Kuhn. It is is available on CRAN: https://CRAN.R-project.org/package=desirability, DOI: https://doi.org/10.32614/CRAN.package.desirability
Metadata
Release files for spotdesirability 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| spotdesirability-0.1.1.tar.gz | 29.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| spotdesirability-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 60.1 kB
Release files / spotdesirability-0.1.1.tar.gz
| Download URL | spotdesirability-0.1.1.tar.gz |
|---|---|
| Size | 29.2 kB |
| Tags | Source |
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Transparency logRelease files / spotdesirability-0.1.1-py3-none-any.whl
| Download URL | spotdesirability-0.1.1-py3-none-any.whl |
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| Size | 30.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Mar 30, 2026.
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