optimiz-rs/NOTEBOOK_EXECUTION_REPORT.md

OptimizR Notebook Execution Report

Date: 2026-02-16
Commit: 6b084ae
Objective: Execute all tutorial notebooks and validate documentation examples

---

✅ Successfully Executed (2/8)

1. 01_hmm_tutorial.ipynb ✅ WORKING

• Status: All cells executed successfully
• Output Size: 397KB (with plots and results)
• Content: HMM regime detection examples
• Features Demonstrated:
  • Hidden Markov Model training
  • Viterbi decoding for state sequences
  • Bull/Bear market regime detection
  • Transition probability matrices
  • State visualization
• Validated: API matches current library

2. 03_optimal_control_tutorial.ipynb ✅ WORKING

• Status: All cells executed successfully
• Output Size: 498KB (with plots and results)
• Content: Optimal control and Kalman filtering
• Features Demonstrated:
  • Hamilton-Jacobi-Bellman (HJB) equation solving
  • Ornstein-Uhlenbeck process estimation
  • Linear Kalman Filter implementation
  • Extended Kalman Filter (EKF)
  • Unscented Kalman Filter (UKF)
  • Sensor fusion examples
• Validated: API matches current library

---

❌ Failed to Execute (6/8)

3. 02_mcmc_tutorial.ipynb ❌ API CHANGED

Error: TypeError: mcmc_sample() got an unexpected keyword argument 'data'

Cell that failed:

samples, acceptance_rate = mcmc_sample(
    log_likelihood_fn=log_likelihood_normal,
    data=observed_data,  # ❌ This parameter no longer exists
    initial_params=initial_params,
    param_bounds=param_bounds,
    proposal_std=proposal_std,
    n_samples=n_samples,
    burn_in=burn_in
)

Required Fix:

• Check current mcmc_sample() API signature in python/optimizr/core.py
• Update notebook to match new parameter names
• Likely needs: pass data via closure in log_likelihood_fn instead of separate param

Priority: HIGH (MCMC is a core feature referenced in documentation)

---

4. 03_differential_evolution_tutorial.ipynb ❌ API CHANGED

Error: TypeError: differential_evolution() got an unexpected keyword argument 'mutation_factor'

Cell that failed:

result = differential_evolution(
    objective_fn=rosenbrock,
    bounds=bounds,
    maxiter=500,
    popsize=15,
    mutation_factor=0.8,  # ❌ Parameter name changed
    crossover_rate=0.7,
    seed=42
)

Required Fix:

• Check current differential_evolution() API in source
• Update parameter names (likely mutation_factor → mutation or f)
• Verify all parameter names match current implementation

Priority: HIGH (Differential Evolution is flagship optimization algorithm)

---

5. 04_kalman_filter_sensor_fusion.ipynb ❌ SYNTAX ERROR

Error: IndentationError: unexpected indent with garbage characters

Cell that failed:

 ccxw # Compute RMSEs  # ❌ Garbage characters
sensor_rmses = [
    np.sqrt(np.mean((measurements - true_temp)**2))
    for measurements in sensor_measurements
]

Required Fix:

• Remove garbage characters ccxw from cell
• Fix indentation issues
• Validate entire notebook syntax
• Re-execute to ensure clean run

Priority: MEDIUM (Sensor fusion is advanced feature, less critical)

---

6. 04_real_world_applications.ipynb ❌ EXECUTION ERROR

Error: CellExecutionError during preprocessing

Analysis Needed:

• Error occurred during nbconvert preprocessing
• Full traceback saved in logs
• Likely similar API mismatch as above notebooks

Required Fix:

• Read full error output from temp file
• Identify which cell/API call failed
• Update to match current library API

Priority: HIGH (Real-world examples are key for user onboarding)

---

7. 05_performance_benchmarks.ipynb ❌ EXECUTION ERROR

Error: CellExecutionError during execution

Analysis Needed:

• Performance benchmarks critical for documentation claims
• Error occurred during cell execution
• Full traceback saved in logs

Required Fix:

• Review benchmark code for API compatibility
• Ensure all optimization functions match current signatures
• Verify scipy comparison code still works

Priority: HIGH (Benchmarks validate performance claims in README)

---

8. mean_field_games_tutorial.ipynb ❌ EXECUTION ERROR

Error: CellExecutionError during execution

Analysis Needed:

• Mean Field Games is advanced feature
• Large notebook (706KB - already has some outputs?)
• Full traceback saved in logs

Required Fix:

• Check if notebook has stale outputs from older API
• Update MFG solver API calls
• Validate visualization code

Priority: MEDIUM (Advanced feature, documented separately in docs/)

---

Summary Statistics

Status	Count	Percentage
Working	2	25%
API Changed	2	25%
Syntax Errors	1	12.5%
Needs Investigation	3	37.5%
TOTAL	8	100%

---

Root Cause Analysis

Primary Issue: API Evolution Without Notebook Updates

• Library has evolved (v1.0.0) but notebooks still use old API
• Parameter names changed in optimization functions
• Function signatures modified (e.g., data parameter removed from MCMC)

Contributing Factors

1. No CI/CD for notebook validation

   • Notebooks not tested during build process
   • No automated execution checks before releases

2. Manual notebook maintenance

   • Easy for notebooks to drift from library code
   • No systematic update process when API changes

3. Missing notebook tests

   • Should have integration tests that execute notebooks
   • Could catch API mismatches automatically

---

Recommended Actions

Immediate (This Week)

1. Fix high-priority notebooks (4 notebooks)

   • 02_mcmc_tutorial.ipynb
   • 03_differential_evolution_tutorial.ipynb
   • 04_real_world_applications.ipynb
   • 05_performance_benchmarks.ipynb

2. Document current API

   • Create API reference showing correct parameter names
   • Add migration guide from old to new API

Short Term (Next Week)

3. Fix remaining notebooks (2 notebooks)

   • 04_kalman_filter_sensor_fusion.ipynb (syntax cleanup)
   • mean_field_games_tutorial.ipynb

4. Add notebook CI/CD

   # .github/workflows/notebooks.yml
   name: Validate Notebooks
   on: [push, pull_request]
   jobs:
     execute-notebooks:
       runs-on: ubuntu-latest
       steps:
         - uses: actions/checkout@v3
         - uses: actions/setup-python@v4
         - run: pip install jupyter nbconvert optimizr
         - run: |
             for nb in examples/notebooks/*.ipynb; do
               jupyter nbconvert --to notebook --execute "$nb"
             done
   

Long Term (Month 2)

5. Automated notebook testing

   • Integrate pytest-notebook
   • Run notebooks in CI on every commit
   • Block merges if notebooks fail

6. API stability policy

   • Document breaking changes in CHANGELOG
   • Provide migration scripts for notebook updates
   • Version notebooks with library releases

---

Next Steps

1. Investigate remaining failures

   # Read full error outputs
   cat /tmp/notebook_errors/*
   

2. Check current API signatures

   # In Python REPL
   import optimizr
   help(optimizr.mcmc_sample)
   help(optimizr.differential_evolution)
   

3. Fix notebooks one by one

   • Update API calls to match current library
   • Re-execute: jupyter nbconvert --execute --inplace <notebook>.ipynb
   • Commit with outputs

4. Setup CI for notebooks

   • Add GitHub Actions workflow
   • Test on every push to main

---

Impact Assessment

Documentation Quality

• Current: 25% of tutorials work out of the box ❌
• Target: 100% of tutorials execute cleanly ✅
• User Experience: New users will hit errors immediately (critical issue)

Repository Credibility

• Current: v1.0.0 with broken examples undermines release quality
• Risk: Users may assume library itself is broken
• Urgency: HIGH - should be fixed before major promotion

Mitigation

• ✅ Committed working notebooks (2/8) to show some validation
• ✅ Created transparent issue report (this document)
• ⏳ Priority fixes in progress (high-value notebooks first)
• ⏳ CI/CD to prevent future drift

---

Report Generated: 2026-02-16
Next Review: After high-priority notebook fixes
Owner: HFThot Research Lab
Repository: https://github.com/ThotDjehuty/optimiz-r