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.
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}.")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.
There are more Python packages for comparison-based learning:
- metric-learn is a collection of algorithms for metric learning. The weakly supervised algorithms learn from triplets and quadruplets.
- 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.
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.
This library is free to use, share, and adapt under the MIT License conditions.
If you publish work that uses this library, please cite our JOSS paper.