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A new version, v1.0.2 of PyTorch Tabular has been released. The library now includes a new method in TabularModel for enabling feature importance. Feature Importance has been enabled for FTTransformer and GATE models. The release note was generated by ChatGPT using the git commit logs from the last release, wrote Manu Joseph the creator of PyTorch Tabular, GATE and LAMA-Net on LinkedIn.
Check out the GitHub repository to learn more about the update.
PyTorch Tabular is a deep learning library that makes working with deep learning and tabular data easy and fast. The library has been built on frameworks PyTorch and PyTorch Lightning, and it works on pandas data frames directly.
The framework makes the standard modeling pipeline simple enough for practitioners while also being reliable enough for production use. It also focuses on customization so that it can be used in a variety of research settings. The below picture depicts the structure of the framework.
Latest Additions
The latest enhancements in the library include several updates.
Firstly, two additional parameters have been added to the GATE model, expanding its functionality and versatility. Additionally, the library configuration now includes the metric_prob_input parameter, providing improved control over metrics within the models. The GATE model has undergone slight improvements, including adjustments to defaults that enhance its overall performance.
Furthermore, various minor bug fixes and improvements have been implemented, such as the addition of accelerator options in the configuration and enhancements to the progress bar. Alongside these enhancements, the library has been updated with newer versions of dependencies, including docformatter, pyupgrade, and ruff-pre-commit. These updates contribute to the library’s overall reliability, functionality, and performance.
Read: How to Handle Tabular Data for Deep Learning Using PyTorch Tabular?
The latest version has been released four months after v1.0.1. Other improvements include various code optimizations, bug fixes, and CI enhancements. For more details, refer to the commits on the library’s GitHub repository.
