libauc 1.0.2

AUC optimization

LibAUC

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

Why is 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 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

Original Links

• Repository: https://github.com/yzhuoning/libauc
• Library website: https://libauc.org

How to install

$ pip install libauc

Example

1. Download the required datasets.
2. Run the following commands

$ python

>>> from libauc import *
>>> ...
>>> Losss = AUCMLoss(imratio=0.1)
>>> optimizer = PESG(model, a=Loss.a, b=Loss.b, alpha=Loss.alpha, lr=0.1, gamma=500, weight_decay=1e-5)
>>> ...
>>> loss = Loss(y_pred, targets)
>>> optimizer.zero_grad()
>>> loss.backward(retain_graph=True)
>>> optimizer.step()

Maintainer

• Zhuoning Yuan [e-mail]