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https://github.com/cblearn/cblearn
Comparison-based Machine Learning in Python
https://github.com/cblearn/cblearn
embedding machine-learning machinelearning multidimensional multidimensional-scaling non-metric ordinal scaling scikit-learn
Last synced: 3 months ago
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Comparison-based Machine Learning in Python
- Host: GitHub
- URL: https://github.com/cblearn/cblearn
- Owner: cblearn
- License: mit
- Created: 2020-08-13T09:54:22.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-05-16T15:52:48.000Z (8 months ago)
- Last Synced: 2024-05-16T17:04:29.436Z (8 months ago)
- Topics: embedding, machine-learning, machinelearning, multidimensional, multidimensional-scaling, non-metric, ordinal, scaling, scikit-learn
- Language: Python
- Homepage:
- Size: 2.64 MB
- Stars: 17
- Watchers: 6
- Forks: 7
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
## Comparison-based Machine Learning in Python
[](https://doi.org/10.21105/joss.06139)
[](https://pypi.python.org/pypi/cblearn)
[](https://cblearn.readthedocs.io/en/stable/?badge=stable)
[](https://github.com/cblearn/cblearn/actions/workflows/test.yml)
[](https://codecov.io/gh/cblearn/cblearn)
Comparison-based learning methods are machine learning algorithms using similarity comparisons ("A and B are more similar than C and D") instead of featurized data.
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from cblearn.datasets import make_random_triplets
from cblearn.embedding import SOE
X = load_iris().data
triplets = make_random_triplets(X, result_format="list-order", size=2000)
estimator = SOE(n_components=2)
# Measure the fit with scikit-learn's cross-validation
scores = cross_val_score(estimator, triplets, cv=5)
print(f"The 5-fold CV triplet error is {sum(scores) / len(scores)}.")
# Estimate the scale on all triplets
embedding = estimator.fit_transform(triplets)
print(f"The embedding has shape {embedding.shape}.")
```
## Getting Started
* [Installation & Quickstart](https://cblearn.readthedocs.io/en/stable/getting_started/index.html)
* [Examples](https://cblearn.readthedocs.io/en/stable/generated_examples/index.html).
* [User Guide](https://cblearn.readthedocs.io/en/stable/user_guide/index.html).
## Contribute
We are happy about your bug reports, questions or suggestions as Github Issues and code or documentation contributions as Github Pull Requests.
Please see our [Contributor Guide](https://cblearn.readthedocs.io/en/stable/contributor_guide/index.html).
## Related packages
There are more Python packages for comparison-based learning:
- [metric-learn](http://contrib.scikit-learn.org/metric-learn) is a collection of algorithms for metric learning. The *weakly supervised* algorithms learn from triplets and quadruplets.
- [salmon](https://docs.stsievert.com/salmon/) is a package for efficiently collecting triplets in crowd-sourced experiments. The package implements ordinal embedding algorithms and sampling strategies to query the most informative comparisons actively.
## Authors and Acknowledgement
*cblearn* was initiated by current and former members of the [Theory of Machine Learning group](http://www.tml.cs.uni-tuebingen.de/index.php) of Prof. Dr. Ulrike von Luxburg at the University of Tübingen.
The leading developer is [David-Elias Künstle](http://www.tml.cs.uni-tuebingen.de/team/kuenstle/index.php).
We want to thank all the contributors here on GitHub.
This work has been supported by the Machine Learning Cluster of Excellence, funded by EXC number 2064/1 – Project number 390727645. The authors would like to thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting David-Elias Künstle.
## License
This library is free to use, share, and adapt under the [MIT License](https://github.com/cblearn/cblearn/blob/master/LICENSE) conditions.
## Citation
Please cite our [JOSS paper](https://joss.theoj.org/papers/10.21105/joss.06139#) if you publish work using `cblearn`:
**Künstle et al., (2024). cblearn: Comparison-based Machine Learning in Python. Journal of Open Source Software, 9(98), 6139, https://doi.org/10.21105/joss.06139**
```
@article{Künstle2024,
doi = {10.21105/joss.06139},
url = {https://doi.org/10.21105/joss.06139},
year = {2024},
publisher = {The Open Journal},
volume = {9}, number = {98}, pages = {6139},
author = {David-Elias Künstle and Ulrike von Luxburg},
title = {cblearn: Comparison-based Machine Learning in Python},
journal = {Journal of Open Source Software}
}
```