cblearn

Comparison-based Machine Learning in Python

Comparison-based Learning algorithms are the Machine Learning algorithms to use when training data contains similarity comparisons ("A and B are more similar than C and D") instead of data points.

Triplet comparisons from human observers help model the perceived similarity of objects. These human triplets are collected in studies, asking questions like "Which of the following bands is most similar to Queen?" or "Which color appears most similar to the reference?".

This library provides an easy-to-use interface for comparison-based learning algorithms. It plays hand-in-hand with scikit-learn:

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}.")

Please try the Examples.

Getting Started

Install cblearn as described here and try the examples.

Find a theoretical introduction to comparison-based learning, the datatypes, algorithms, and datasets in the User Guide.

Features

Datasets

cblearn provides utility methods to simplify the loading and conversion of your comparison datasets. In addition, some functions download and load multiple real-world comparisons.

Embedding Algorithms

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.

Authors and Acknowledgement

cblearn was initiated by current and former members of the Theory of Machine Learning group of Prof. Dr. Ulrike von Luxburg at the University of Tübingen. The leading developer is David-Elias Künstle.

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 under the MIT License conditions. Please cite this library appropriately if it contributes to your scientific publication. We would also appreciate a short email (optionally) to see how our library is being used.

Changelog

Upcoming

0.2.0

• 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

0.1.2

• 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.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.