dlordinal: a Python package for deep ordinal classification
This package provides a unified tool for researchers and practitioners working on ordinal classification problems, but it is incremental as it aggregates existing methodologies.
The authors introduced dlordinal, a Python package that unifies state-of-the-art deep learning techniques for ordinal classification, implementing various methods like loss functions and output layers to leverage ordering information in target variables.
dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal.