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.
479 Dec 25, 2022
18 Nov 21, 2022
128 Dec 02, 2022
1 Nov 09, 2021
27 Oct 14, 2022
19 Sep 20, 2022
235 Jan 05, 2023
529 Jan 03, 2023
12 Jan 01, 2023
769 Dec 08, 2022
41 Dec 12, 2022
27 Dec 13, 2022
23 Nov 02, 2022
25 Aug 11, 2021
71 Jan 05, 2023
255 Oct 17, 2022
20.4k Dec 30, 2022
121 Jan 06, 2023
31 Dec 11, 2022
133 Dec 30, 2022