Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
Take the provided Python code and enhance with comprehensive logging capabilities. The logging should include:
DEBUG level logs:
log data entities being processed.
Track progress through critical sections of the code.
INFO level logs:
Highlight high-level progress milestones.
WARNING and ERROR level logs:
Capture detailed error messages for troubleshooting.
Instructions
Focus solely on the xyz method within the provided code.
Introduce logging statements to meet the specified requirements.
Deduplicate any redundant log statements to maintain code efficiency.
Do not alter the existing implementation or logic.
Remove or replace print statements with log statements
Add any private methods needed to make objects printable
Logging Guidelines
Use Python's built-in logging module.
logger is already defined in the class. No need to configure logging.
Code Enhancement Expectations
The enhanced code should maintain its original functionality.
Logging statements should be strategically placed for optimal debugging and monitoring.
The code should remain readable and maintainable.
Deliverables
The modified Python code with enhanced logging in all methods.
A brief explanation of the logging strategy and configuration used.
Evaluation Criteria
Correctness: Logging statements accurately capture required information.
Code quality: Enhanced code maintains readability and maintainability.
Logging effectiveness: Logs provide valuable insights for debugging and monitoring.
Project Robyn
Generated logging with prompt below.
Task Description