Metadata-Version: 2.4
Name: cblearn
Version: 0.4.0
Summary: Comparison-based Machine Learning in Python.
Home-page: https://github.com/cblearn/cblearn
Author: David-Elias Künstle
Author-email: david-elias.kuenstle@uni-tuebingen.de
Maintainer: Guillermo Aguilar, Vivek Anand
Maintainer-email: Guillermo Aguilar <gmo.aguilar.c@gmail.com>, Vivek Anand <vivekanand@gatech.edu>
Project-URL: Documentation, https://cblearn.readthedocs.io
Project-URL: Source, https://github.com/cblearn/cblearn
Project-URL: Issue Tracker, https://github.com/cblearn/cblearn/issues
Project-URL: Paper, https://doi.org/10.21105/joss.06139
Classifier: Development Status :: 4 - Beta
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Classifier: Intended Audience :: Science/Research
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Classifier: Programming Language :: Python
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License-File: LICENSE
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Dynamic: license-file

<h1 align="center">
<img src="https://raw.githubusercontent.com/cblearn/cblearn/main/docs/logo-light.png" width="300">
</h1><br>

## Comparison-based Machine Learning in Python
[![DOI](https://joss.theoj.org/papers/10.21105/joss.06139/status.svg)](https://doi.org/10.21105/joss.06139)
[![PyPI version](https://img.shields.io/pypi/v/cblearn.svg)](https://pypi.python.org/pypi/cblearn)
[![Documentation](https://readthedocs.org/projects/cblearn/badge/?version=stable)](https://cblearn.readthedocs.io/en/stable/?badge=stable)
[![Test status](https://github.com/cblearn/cblearn/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/cblearn/cblearn/actions/workflows/test.yml)
[![Test Coverage](https://codecov.io/gh/cblearn/cblearn/branch/main/graph/badge.svg?token=P9JRT6OK6O)](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.

**Author:** [David-Elias Künstle](http://www.tml.cs.uni-tuebingen.de/team/kuenstle/index.php)

**Maintainers:** [Guillermo Aguilar](https://github.com/guillermoaguilar), [Vivek Anand](https://github.com/Vivdaddy)

**Contributors:** [Alexander Conzelmann](https://github.com/Conzel), [Michaël Perrot](https://github.com/mperrot), [Mojtaba Barzegari](https://github.com/mbarzegary)

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/main/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} 
} 
```

# Changelog

## Upcoming

## 0.4

- Feature: `embedding.LORE`, a low-rank ordinal embedding that estimates the intrinsic dimensionality
- Feature: Support for Python 3.12 and 3.13
- Improvement: Compatibility with scikit-learn 1.6 and newer, which replaced the estimator tag dictionary
  by the `__sklearn_tags__` API. scikit-learn 1.6 is now the minimum supported version.
- Improvement: Readable errors for invalid embedding dimensions, raised in `fit` instead of `__init__`,
  as required by scikit-learn
- Improvement: Validation of `test_dimensions` in `embedding.estimate_dimensionality_cv`
- Improvement: The food, nature and vogue datasets are downloaded from OSF mirrors,
  since the original hosts no longer serve the archives
- Improvement: Externally hosted archives are pinned to fixed files or commits,
  so that their checksums no longer change on every upstream push
- Fix: Gradient of the STE embedding, which weighted each triplet by P * (1 - P) instead of (1 - P)
- Fix: `estimate_dimensionality_cv` returns the estimated dimension as a Python int
- Fix: `utils.torch_device` returns and validates an explicit device instead of None
- Fix: Sparse input is detected with `scipy.sparse.issparse`, so that the newer sparray classes are recognized
- Fix: The numpy converter for rpy2 is registered directly, since `numpy2ri.activate` raises in rpy2 3.5.12 and newer
- Others: Extended unit tests and coverage, seeded triplet sampling in the test suite

## 0.3

- Feature: JOSS paper
- Feature: Quickstart guide in documentation
- Feature: Data point sampling from manifolds.
- Improvement: Extended documentation
- Improvement: cblearn logo and new style in documentation
- Improvement: Filter invalid responses in datasets
- Improvement: Full compatibility to sklearn estimator tests
  
## 0.2

- Improvement: Extended documentation
- Feature: `embedding.estimate_dimensionality_cv` function (Künstle et al., 2022)
- Fix: Avoid numpy deprecation warning for scalar variables in `fetch_similarity_matrix`
- Fix: Various errors in the examples
- Fix: Minor errors in the unit tests
- Others: Updated dependencies

## 0.1
### 0.1.2

- support python 3.11
- update core dependencies
  
### 0.1.1

- Minor fixes in the documentation.
- Adapt loading of food and imagenet dataset to solve problems caused by changes in externally hosted files
  
### 0.1.0

- Support python 3.9 and 3.10.
- Introduce semantic versioning
- Publish to PyPI

MIT License

Copyright (c) 2020-2021 The cblearn developers.

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of this software and associated documentation files (the "Software"), to deal
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