Release 0.12.0

New features:

• Enhancements to Foundation Model training
  Improvements to the foundation model training process, leading to better performance in downstream applications.

  • Simplified configuration by removing the need to predefine certain time-based parameters incl.check_target_for_next_n_days parameter.
    This requires changes to previously written configuration files.
  • Optimized training with an updated optimizer that eliminates the need for manual learning rate scheduling.
  • Improved feature representation through automated parameter tuning.
  • Performance gains from optimized data handling and more efficient training strategies.
    
    

• Improved Parquet File Support
  Faster and more scalable data processing with an upgraded backend engine.

  • Enhanced memory management and stability for large-scale data.
  • Expanded support for advanced query operations on parquet data sources.
    
    

• Interpretability
  Added support for event-level interpretability in classification and regression models.
  
  

Fixes

• Fixed an issue where the _FINISHED flag for column fit tasks was occasionally set incorrectly, resulting in an unstable resume option.
  
  
• Fixed an issue where the log in main.log file was empty, incomplete or incorrectly written during model training.
  
  
• Fixed an issue where `next_n_hours' parameter used in target function was excluding starting time stamp.
  
  
• Updated packages to improve performance, improve security and for compatibility with the latest features.