Release 0.19

New Features

• Time window slicing for event data
  Event data sources can now be restricted to a defined start and end timestamp with the new slice_time_window function. This allows analyses and training runs to focus on specific periods (e.g., campaigns, weeks, or test windows) without extra preprocessing.

• Direct access to date columns
  Event data sources now expose a date column (timestamps), allowing time-based filtering and grouping with filter() and groupBy() functions.

Improvements

• Simplified interpretation output
  The interpret function now produces cleaner results by removing non-essential fields, making logs and monitoring pipelines easier to read and consume.

• Improved window shuffling defaults
  The buffer size has been changed to 100k, enhancing randomness in shuffled windows and improving generalization during training.

Fixes

• Loss computation – refined loss calculations in downstream models.
• Module visibility – ensured consistent access to essential modules.
• Date handling – added support for numeric date formats and fixed timestamp edge cases.
• Split point generation – restricted to explicitly defined data sources.

Documentation

Expanded sample target functions
A new set of ready-to-use target functions has been added, to help prototype and compare approaches quicker, and demonstrate how to use new functions.