cblearn 0.0.1

Comparison-based Machine Learning in Python.

cblearn

Comparison-based Machine Learning in Python

:warning: cblearn is work in progress. The API can change and bugs appear. Please help us by posting an issue :construction:

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.

:eyes: VSS 2022: Please find an example of psychophysical scaling with triplets and ordinal embedding here :eyes:

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 colour appears most similar to the reference?".

This library provides an easy to use interface to 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.

Dataset	Query	#Object	#Response	#Triplet
Vogue Cover	Odd-out Triplet	60	1,107	2,214
Nature Scene	Odd-out Triplet	120	3,355	6,710
Car	Most-Central Triplet	60	7,097	14,194
Material	Standard Triplet	100	104,692	104,692
Food	Standard Triplet	100	190,376	190,376
Musician	Standard Triplet	413	224,792	224,792
Things Image Testset	Odd-out Triplet	1,854	146,012	292,024
ImageNet Images v0.1	Rank 2 from 8	1,000	25,273	328,549
ImageNet Images v0.2	Rank 2 from 8	50,000	384,277	5M

Embedding Algorithms

Algorithm	Default	Pytorch (GPU)	Reference Wrapper
Crowd Kernel Learning (CKL)	X	X
FORTE		X
GNMDS	X	X
Maximum-Likelihood Difference Scaling (MLDS)	X		MLDS (R)
Soft Ordinal Embedding (SOE)	X	X	loe (R)
Stochastic Triplet Embedding (STE/t-STE)	X	X

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 would like 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 under the MIT License conditions. Please reference 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

0.0.1

• Versioning and publishing to PyPI MIT License

Copyright (c) 2020-2021 The cblearn developers.

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