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.
249 Jan 03, 2023
213 Dec 27, 2022
57 Dec 29, 2022
105 May 15, 2022
1.8k Jan 07, 2023
25 Aug 12, 2021
32 Oct 18, 2022
116 Dec 20, 2022
5 Jun 08, 2022
18 Dec 30, 2022
29 Oct 28, 2022
254 Jan 05, 2023
5 Aug 25, 2022
46 Dec 22, 2022
26 Sep 16, 2022
3 Nov 01, 2022
85 Dec 11, 2022
2.1k Jan 09, 2023
287 Dec 21, 2022
4 Jan 05, 2022