v2.0.4

Enhancements and New Features

This release introduces several enhancements and new features to Chemprop. A notable addition is a new notebook demonstrating Monte Carlo Tree Search for model interpretability (see here). Enhancements have been made to the output transformation and prediction saving mechanisms for MveFFN and EvidentialFFN. Additionally, users can now perform predictions on CPU even if the models were trained on GPU. Users are now also warned when not using the TensorBoard logger, helping them to be aware of available logging tools for better monitoring.

Bug Fixes

Several bugs have been fixed in this release, including issues related to Matthews Correlation Coefficient (MCC) metrics and loss calculations, and the behavior of the CGR featurizer when the bond features matrix is empty. The task_weights parameter has been standardized across all loss functions and moved to the correct device for MCC metrics, preventing device mismatch errors.

What's Changed

• Standardize task_weights in LossFunction across all loss functions by @shihchengli in #941
• Improve output transformation and prediction saving for MveFFN and EvidentialFFN by @shihchengli in #943
• Enable CPU prediction for GPU-trained models by @snaeppi in #950
• Fix Issues in MCC Metrics and Loss Calculations by @shihchengli in #942
• Fix docs building by pinning sphinx-argparse by @jonwzheng in #964
• Add Monte Carlo Tree search notebook for interpretability by @hwpang in #924
• Fix CGR featurizer behavior when bond features matrix is empty by @jonwzheng in #958
• Fix Failing CI for torch==2.4.0 on Windows ray[tune] Tests by @JacksonBurns in #971
• warn users when not using tensorboard logger by @JacksonBurns in #967
• Bug: Move task_weights to 'device' for MCC metrics by @YoochanMyung in #973

New Contributors

• @snaeppi made their first contribution in #950
• @YoochanMyung made their first contribution in #973

Full Changelog: v2.0.3...v2.0.4