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.
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