An end-to-end machine learning library for AUC optimization.

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

• Repository: https://github.com/yzhuoning/libauc
• Website: https://libauc.org

$ pip install libauc

• 01.Creating Imbalanced Benchmark Datasets (Notebook)
• 02.Training ResNet20 with Imbalanced CIFAR10 (Notebook)
• 03.Training with Pytorch Learning Rate Scheduling (Notebook)
• 04.Training with Imbalanced Datasets on Distributed Setting (Coming soon)

>>> #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.

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}
}

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.