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LibAUC: A Deep Learning Library for X-Risk Optimization

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We continuously update our library by making improvements and adding new features. If you use or like our library, please star:star: this repo. Thank you!

:calendar: Updates

• 2023/5: We are currently working on another major update, which will include new features, documentation & codebase improvements. We expect the new version to be released later this month!
• 2022/7: LibAUC 1.2.0 is released! In this version, we've included more losses and optimizers as well as made some performance improvements. Please check release note for more details! Thanks!

:mag: What is X-Risk?

X-risk refers to a family of compositional measures/losses, in which each data point is compared with a set of data points explicitly or implicitly for defining a risk function. It covers a family of widely used measures/losses including but not limited to the following four interconnected categories:

• Areas under the curves, including areas under ROC curves (AUROC), areas under Precision-Recall curves (AUPRC), one-way and two-wary partial areas under ROC curves.
• Ranking measures/objectives, including p-norm push for bipartite ranking, listwise losses for learning to rank (e.g., listNet), mean average precision (mAP), normalized discounted cumulative gain (NDCG), etc.
• Performance at the top, including top push, top-K variants of mAP and NDCG, Recall at top K positions (Rec@K), Precision at a certain Recall level (Prec@Rec), etc.
• Contrastive objectives, including supervised contrastive objectives (e.g., NCA), and global self-supervised contrastive objectives improving upon SimCLR and CLIP.

:star: Key Features

• Easy Installation - Easy to install and insert LibAUC code into existing training pipeline with Deep Learning frameworks like PyTorch.
• Broad Applications - Users can learn different neural network structures (e.g., MLP, CNN, GNN, transformer, etc) that support their data types.
• Efficient Algorithms - Stochastic algorithms with provable theoretical convergence that support learning with millions of data points without larger batch size.
• Hands-on Tutorials - Hands-on tutorials are provided for optimizing a variety of measures and objectives belonging to the family of X-risks.

:gear: Installation

$ pip install libauc==1.2.0

The latest version 1.2.0 is available now! You can check release note for more details. Source code is available for download here.

:clipboard: Usage

Example training pipline for optimizing X-risk (e.g., AUROC)

>>> #import our loss and optimizer
>>> from libauc.losses import AUCMLoss 
>>> from libauc.optimizers import PESG 
...
>>> #define loss & optimizer
>>> Loss = AUCMLoss()
>>> optimizer = PESG()
...
>>> #training
>>> model.train()    
>>> for data, targets in trainloader:
>>>	data, targets  = data.cuda(), targets.cuda()
        logits = model(data)
	preds = torch.sigmoid(logits)
        loss = Loss(preds, targets) 
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
...	
>>> #update internal parameters
>>> optimizer.update_regularizer()

:notebook_with_decorative_cover: Tutorials

X-Risk

• AUROC: Optimizing AUROC loss on imbalanced dataset
• AUPRC: Optimizing AUPRC loss on imbalanced dataset
• Partial AUROC: Optimizing Partial AUC loss on imbalanced dataset
• Compositional AUROC: Optimizing Compositional AUROC loss on imbalanced dataset
• NDCG: Optimizing NDCG loss on MovieLens 20M
• SogCLR: Optimizing Contrastive Loss using small batch size on ImageNet-1K

Applications

• A Tutorial of Imbalanced Data Sampler (Updates Coming Soon)
• Constructing benchmark imbalanced datasets for CIFAR10, CIFAR100, CATvsDOG, STL10
• Using LibAUC with PyTorch learning rate scheduler
• Optimizing AUROC loss on Chest X-Ray dataset (CheXpert)
• Optimizing AUROC loss on Skin Cancer dataset (Melanoma)
• Optimizing AUROC loss on Molecular Graph dataset (OGB-Molhiv)
• Optimizing multi-label AUROC loss on Chest X-Ray dataset (CheXpert)
• Optimizing AUROC loss on Tabular dataset (Credit Fraud)
• Optimizing AUROC loss for Federated Learning

:page_with_curl: Citation

If you find LibAUC useful in your work, please cite the papers in BibTex and acknowledge our library:

@inproceedings{yuan2023libauc,
	title={LibAUC: A Deep Learning Library for X-risk Optimization},
	author={Zhuoning Yuan and Dixian Zhu and Zi-Hao Qiu and Gang Li and Xuanhui Wang and Tianbao Yang},
	booktitle={29th SIGKDD Conference on Knowledge Discovery and Data Mining},
	year={2023}
	}

@article{yang2022algorithmic,
   title={Algorithmic Foundation of Deep X-Risk Optimization},
   author={Yang, Tianbao},
   journal={arXiv preprint arXiv:2206.00439},
   year={2022}
}

:email: Contact

For any technical questions, please open a new issue in the Github. If you have any other questions, please contact us @ Zhuoning Yuan [yzhuoning@gmail.com] and Tianbao Yang [tianbao-yang@tamu.edu].