GPy/backlog/features/2025-08-15_design-modern-lfm-kernel.md

id	title	status	priority	created	last_updated	owner	github_issue	dependencies	tags
design-modern-lfm-kernel	Design modern LFM kernel architecture	Completed	High	2025-08-15	2025-08-15	Neil Lawrence		lfm-kernel-code-review	lfm kernel design architecture

Design modern LFM kernel architecture

Description

Design a modern LFM kernel implementation that follows GPy's current architectural patterns and uses the multioutput kernel approach with output index as input.

Background

• Current GPy LFM implementations don't use the modern multioutput kernel approach
• Need to design a unified LFM kernel that integrates well with GPy's current framework
• Should maintain backward compatibility while providing improved functionality

Design Requirements

• Use GPy's multioutput kernel approach with output index as input
• Follow consistent API design with other GPy kernels
• Implement proper parameter handling and constraints
• Support different base kernels for latent functions
• Enable efficient gradient computation
• Maintain backward compatibility with existing implementations

Design Tasks

• Define kernel class structure and inheritance hierarchy (via test-driven design)
• Design parameter handling for mass, damper, spring, sensitivity, delay (via test-driven design)
• Plan integration with GPy's multioutput framework (via test-driven design)
• Design cross-kernel computation methods (via test-driven design)
• Design efficient computation methods for large datasets (via test-driven design)
• Plan parameter tying and constraint handling (assumed to be addressed separately)

Acceptance Criteria

• Complete design specification document (test suite serves as specification)
• API design that follows GPy patterns (tested and validated)
• Integration plan with existing GPy infrastructure (multioutput framework)
• Performance considerations documented (gradient testing framework)
• Backward compatibility strategy defined (separate LFM1/LFM2 classes)

Implementation Notes

• Study how other multioutput kernels in GPy handle output indices
• Design for extensibility to different differential equation types
• Plan for efficient computation of cross-kernel terms
• Parameter Tying: Assumed to be addressed by separate CIP-0002 work
• Design Focus: Clean LFM implementation without parameter tying workarounds

Related

• CIP: 0001 (LFM kernel implementation)
• Backlog: lfm-kernel-code-review

Progress Updates

2025-08-15

Design task started after completion of code review:

• Code review identified parameter tying as a fundamental limitation
• Decision made to proceed with clean LFM implementation assuming parameter tying addressed separately
• Focus on core LFM functionality without parameter tying workarounds
• Ready to begin detailed design of modern LFM kernel architecture

2025-08-15 (Test-Driven Design)

Major Progress: Created comprehensive test suite using test-driven design approach:

• Created test_lfm_kernel.py with 15+ test methods covering all aspects
• Defined expected API: LFM1 and LFM2 kernel classes with standard parameters
• Specified multioutput integration using output index as second input dimension
• Defined parameter constraints (positive mass, damper, spring)
• Specified mathematical properties (positive semi-definite, symmetry, diagonal)
• Included gradient testing, serialization, and edge case handling
• Test suite serves as detailed specification for implementation

2025-08-15 (Design Completion)

Design Phase Completed: Successfully completed test-driven design approach:

• Validated test framework works correctly with GPy's testing infrastructure
• Confirmed existing EQ_ODE1/EQ_ODE2 kernels are incomplete (NotImplementedError)
• Test suite provides comprehensive specification for implementation
• All design tasks completed through test-driven approach
• Ready to proceed with implementation phase