LibAUC
An end-to-end machine learning library for AUC optimization.
Why LibAUC?
Deep AUC Maximization (DAM) is a paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. There are several benefits of maximizing AUC score over minimizing the standard losses, e.g., cross-entropy.
- In many domains, AUC score is the default metric for evaluating and comparing different methods. Directly maximizing AUC score can potentially lead to the largest improvement in the model’s performance.
- Many real-world datasets are usually imbalanced . AUC is more suitable for handling imbalanced data distribution since maximizing AUC aims to rank the predication score of any positive data higher than any negative data
Links
- Repository: https://github.com/yzhuoning/libauc
- Website: https://libauc.org
Installation
$ pip install libauc
Usage
Official Tutorials:
- 01.Creating Imbalanced Benchmark Datasets [Notebook][Script]
- 02.Training ResNet20 with Imbalanced CIFAR10 [Notebook][Script]
- 03.Training with Pytorch Learning Rate Scheduling [Notebook][Script]
- 04.Training with Imbalanced Datasets on Distributed Setting [Coming soon]
Quickstart for beginner:
>>> #import library
>>> from libauc.losses import AUCMLoss
>>> from libauc.optimizers import PESG
...
>>> #define loss
>>> Loss = AUCMLoss(imratio=0.1)
>>> optimizer = PESG(imratio=0.1)
...
>>> #training
>>> model.train()
>>> for data, targets in trainloader:
>>> data, targets = data.cuda(), targets.cuda()
preds = model(data)
loss = Loss(preds, targets)
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
...
>>> #restart stage
>>> optimizer.update_regularizer()
...
>>> #evaluation
>>> model.eval()
>>> for data, targets in testloader:
data, targets = data.cuda(), targets.cuda()
preds = model(data)
Please visit our website or github for more examples.
Citation
If you find LibAUC useful in your work, please cite the following paper:
@article{yuan2020robust,
title={Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification},
author={Yuan, Zhuoning and Yan, Yan and Sonka, Milan and Yang, Tianbao},
journal={arXiv preprint arXiv:2012.03173},
year={2020}
}
Contact
If you have any questions, please contact us @ Zhuoning Yuan [[email protected]] and Tianbao Yang [[email protected]] or please open a new issue in the Github.
141 Oct 13, 2022
5 Nov 07, 2022
88 Jan 04, 2023
9 Sep 01, 2022
4 Sep 19, 2022
47 Jan 03, 2023
1.8k Jan 07, 2023
10 Jan 01, 2023
3 Aug 26, 2022
5.2k Dec 28, 2022
171 Dec 26, 2022
12 Dec 15, 2022
543 Dec 30, 2022
14 Dec 08, 2022
910 Dec 28, 2022
14 Nov 28, 2022
676 Dec 31, 2022
3 Sep 30, 2021
87 Jan 01, 2023
62 Nov 19, 2022