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.
Generated logging for visualization classes with prompt below
Task Description
Take the provided code and enhance it with comprehensive logging capabilities.
The logging should include:
DEBUG level logs: - Print 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 requested module/file/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. Do not remove docstring
Remove print statements or replace them with logging statements
Utilize str method from dataclasses if necessary to log information
Add any private methods needed to make objects printable Logging Guidelines
Use Python's built-in logging module.
logger config is already defined for the project. Just instantiate logger if it is not already there.
Use lazy formatting for logging functions
Assume data entities have str method implemented.
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.
Project Robyn
Generated logging for visualization classes with prompt below
Task Description
The logging should include: DEBUG level logs: - Print data entities being processed.
WARNING and ERROR level logs:
Instructions
Code Enhancement Expectations
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.