libauc 1.1.9rc3

LibAUC: A Deep Learning Library for X-Risk Optimization

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!

What is X-Risks?

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, which can be organized into 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.

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.

Installation

$ pip install libauc==1.1.9rc3

The latest version 1.1.9rc3 is updated now! You can also download source code for previous version here.

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()

Tutorials

For tutorials, please visit https://github.com/Optimization-AI/LibAUC.

Citation

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

@misc{libauc2022,
      title={LibAUC: A Deep Learning Library for X-Risk Optimization.},
      author={Zhuoning Yuan, Zi-Hao Qiu, Gang Li, Dixian Zhu, Zhishuai Guo, Quanqi Hu, Bokun Wang, Qi Qi, Yongjian Zhong, Tianbao Yang},
      year={2022}
	}

@article{dox22,
	title={Algorithmic Foundation of Deep X-risk Optimization},
	author={Tianbao Yang},
	journal={CoRR},
	year={2022}

Contact

For any technical questions, please open a new issue in the Github. If you have any other questions, please contact us @ Zhuoning Yuan [[e-mail]] and Tianbao Yang [[e-mail]].