cblearn 0.3.0
Comparison-based Machine Learning in Python.
Requires Python: >=3.9

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<img src="https://raw.githubusercontent.com/cblearn/cblearn/main/docs/logo-light.png" width="300">
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## Comparison-based Machine Learning in Python
[![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/master/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
from cblearn.metrics import QueryScorer

X = load_iris().data
triplets = make_random_triplets(X, result_format="list-order", size=1000)

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.

If you publish work that uses this library, please cite our JOSS paper.

# Changelog

## Upcoming

## 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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