Random State Threading

[jeremymanning]

Parent Epic: #155

Task: Random State Threading

Description

Propagate the random_state parameter through all stochastic operations in both TFA and HTFA classes to ensure reproducible results.

Acceptance Criteria

• K-means initialization uses random_state
• Factor initialization uses random_state
• All scipy/sklearn calls receive random_state
• Results are reproducible with same random_state
• None random_state still works (non-deterministic)

Technical Details

• Update htfa/core/tfa.py K-means initialization
• Modify factor initialization in _initialize_factors method
• Thread through to scipy.sparse.linalg functions
• Pass to sklearn clustering/decomposition calls
• Create RandomState object once and reuse

Dependencies

• Task 164 must be complete (parameters added)

Effort Estimate

• Size: M
• Hours: 6-8
• Parallel: false

Definition of Done

• Code implemented in htfa/core/tfa.py and htfa/core/htfa.py
• Test for reproducibility added
• Verified same seed produces same results
• Documentation updated
• Code reviewed

Status

✅ COMPLETED - All stochastic operations now use random_state for full reproducibility.

[jeremymanning]

✅ Task Completed - 2025-09-10

🎯 All Acceptance Criteria Met

• ✅ K-means initialization uses random_state
• ✅ Factor initialization uses random_state
• ✅ All scipy/sklearn calls receive random_state
• ✅ Results are reproducible with same random_state
• ✅ None random_state still works (non-deterministic)

📦 Implementation Summary

Successfully implemented random state threading across TFA and HTFA classes through 3 parallel work streams:

Stream A - TFA Random State Implementation:

• Updated KMeans initialization from hardcoded random_state=42 to use class's random_state
• Fixed voxel and timepoint subsampling to use self.random_state.choice
• Updated data-based factor initialization to use self.random_state.randn
• Fixed bug in factor initialization shape

Stream B - HTFA Random State Propagation:

• Added validate_random_state import from validation module
• Modified HTFA __init__ to validate random_state parameter
• Updated _initialize_subject_models() to pass random_state to all TFA instances
• Ensured complete reproducibility across the hierarchy

Stream C - Scipy Integration and Testing:

• Analyzed all scipy usage - found only deterministic functions (no random_state needed)
• Created comprehensive test suite with 13 reproducibility tests
• Verified reproducibility with fixed seeds and non-deterministic behavior with None
• All tests passing successfully

🧪 Testing

• Unit tests: ✅ 13 new reproducibility tests created and passing
• Integration tests: ✅ Verified TFA/HTFA integration
• Manual testing: ✅ Confirmed same seed produces identical results

💻 Recent Commits

• 9cd5822 - Issue Random State Threading #157: Add random_state support to TFA class
• 0c9c3e2 - Issue Random State Threading #157: Propagate random_state in HTFA to TFA instances
• ee8f948 - Issue Random State Threading #157: Add reproducibility tests and scipy integration

📚 Documentation

• Code documentation: ✅ Updated docstrings
• Test coverage: ✅ Comprehensive reproducibility test suite created

This task is ready for review and can be closed.

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Task completed: 100% | Synced at 2025-09-10T14:41:21Z

[jeremymanning]

Completed and merged to main in commit b2c3dff