docs: add notebook execution report
Created comprehensive report documenting notebook validation results: - 2/8 notebooks execute successfully (25%) - 2/8 have API mismatches (parameter name changes) - 1/8 has syntax errors (garbage characters) - 3/8 require further investigation The report includes: - Detailed error analysis for each failed notebook - Root cause analysis (API evolution without updates) - Recommended fixes with priorities - Impact assessment on documentation quality - Proposed CI/CD workflow for automated validation Priority: HIGH - Broken examples undermine v1.0.0 credibility. Next steps: Fix high-priority notebooks (MCMC, DE, benchmarks, real-world)
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Melvin Alvarez
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# OptimizR Notebook Execution Report
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**Date**: 2026-02-16
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**Commit**: 6b084ae
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**Objective**: Execute all tutorial notebooks and validate documentation examples
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---
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## ✅ Successfully Executed (2/8)
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### 1. `01_hmm_tutorial.ipynb` ✅ WORKING
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- **Status**: All cells executed successfully
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- **Output Size**: 397KB (with plots and results)
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- **Content**: HMM regime detection examples
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- **Features Demonstrated**:
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- Hidden Markov Model training
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- Viterbi decoding for state sequences
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- Bull/Bear market regime detection
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- Transition probability matrices
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- State visualization
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- **Validated**: API matches current library
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### 2. `03_optimal_control_tutorial.ipynb` ✅ WORKING
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- **Status**: All cells executed successfully
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- **Output Size**: 498KB (with plots and results)
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- **Content**: Optimal control and Kalman filtering
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- **Features Demonstrated**:
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- Hamilton-Jacobi-Bellman (HJB) equation solving
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- Ornstein-Uhlenbeck process estimation
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- Linear Kalman Filter implementation
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- Extended Kalman Filter (EKF)
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- Unscented Kalman Filter (UKF)
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- Sensor fusion examples
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- **Validated**: API matches current library
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---
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## ❌ Failed to Execute (6/8)
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### 3. `02_mcmc_tutorial.ipynb` ❌ API CHANGED
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**Error**: `TypeError: mcmc_sample() got an unexpected keyword argument 'data'`
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**Cell that failed:**
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```python
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samples, acceptance_rate = mcmc_sample(
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log_likelihood_fn=log_likelihood_normal,
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data=observed_data, # ❌ This parameter no longer exists
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initial_params=initial_params,
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param_bounds=param_bounds,
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proposal_std=proposal_std,
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n_samples=n_samples,
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burn_in=burn_in
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)
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```
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**Required Fix**:
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- Check current `mcmc_sample()` API signature in `python/optimizr/core.py`
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- Update notebook to match new parameter names
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- Likely needs: pass data via closure in log_likelihood_fn instead of separate param
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**Priority**: HIGH (MCMC is a core feature referenced in documentation)
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---
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### 4. `03_differential_evolution_tutorial.ipynb` ❌ API CHANGED
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**Error**: `TypeError: differential_evolution() got an unexpected keyword argument 'mutation_factor'`
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**Cell that failed:**
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```python
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result = differential_evolution(
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objective_fn=rosenbrock,
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bounds=bounds,
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maxiter=500,
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popsize=15,
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mutation_factor=0.8, # ❌ Parameter name changed
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crossover_rate=0.7,
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seed=42
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)
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```
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**Required Fix**:
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- Check current `differential_evolution()` API in source
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- Update parameter names (likely `mutation_factor` → `mutation` or `f`)
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- Verify all parameter names match current implementation
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**Priority**: HIGH (Differential Evolution is flagship optimization algorithm)
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---
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### 5. `04_kalman_filter_sensor_fusion.ipynb` ❌ SYNTAX ERROR
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**Error**: `IndentationError: unexpected indent` with garbage characters
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**Cell that failed:**
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```python
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ccxw # Compute RMSEs # ❌ Garbage characters
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sensor_rmses = [
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np.sqrt(np.mean((measurements - true_temp)**2))
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for measurements in sensor_measurements
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]
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```
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**Required Fix**:
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- Remove garbage characters `ccxw` from cell
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- Fix indentation issues
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- Validate entire notebook syntax
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- Re-execute to ensure clean run
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**Priority**: MEDIUM (Sensor fusion is advanced feature, less critical)
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---
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### 6. `04_real_world_applications.ipynb` ❌ EXECUTION ERROR
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**Error**: CellExecutionError during preprocessing
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**Analysis Needed**:
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- Error occurred during nbconvert preprocessing
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- Full traceback saved in logs
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- Likely similar API mismatch as above notebooks
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**Required Fix**:
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- Read full error output from temp file
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- Identify which cell/API call failed
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- Update to match current library API
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**Priority**: HIGH (Real-world examples are key for user onboarding)
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---
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### 7. `05_performance_benchmarks.ipynb` ❌ EXECUTION ERROR
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**Error**: CellExecutionError during execution
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**Analysis Needed**:
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- Performance benchmarks critical for documentation claims
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- Error occurred during cell execution
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- Full traceback saved in logs
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**Required Fix**:
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- Review benchmark code for API compatibility
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- Ensure all optimization functions match current signatures
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- Verify scipy comparison code still works
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**Priority**: HIGH (Benchmarks validate performance claims in README)
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---
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### 8. `mean_field_games_tutorial.ipynb` ❌ EXECUTION ERROR
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**Error**: CellExecutionError during execution
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**Analysis Needed**:
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- Mean Field Games is advanced feature
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- Large notebook (706KB - already has some outputs?)
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- Full traceback saved in logs
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**Required Fix**:
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- Check if notebook has stale outputs from older API
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- Update MFG solver API calls
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- Validate visualization code
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**Priority**: MEDIUM (Advanced feature, documented separately in docs/)
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---
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## Summary Statistics
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| Status | Count | Percentage |
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|--------|-------|------------|
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| **Working** | 2 | 25% |
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| **API Changed** | 2 | 25% |
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| **Syntax Errors** | 1 | 12.5% |
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| **Needs Investigation** | 3 | 37.5% |
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| **TOTAL** | 8 | 100% |
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---
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## Root Cause Analysis
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### Primary Issue: API Evolution Without Notebook Updates
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- Library has evolved (v1.0.0) but notebooks still use old API
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- Parameter names changed in optimization functions
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- Function signatures modified (e.g., `data` parameter removed from MCMC)
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### Contributing Factors
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1. **No CI/CD for notebook validation**
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- Notebooks not tested during build process
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- No automated execution checks before releases
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2. **Manual notebook maintenance**
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- Easy for notebooks to drift from library code
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- No systematic update process when API changes
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3. **Missing notebook tests**
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- Should have integration tests that execute notebooks
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- Could catch API mismatches automatically
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---
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## Recommended Actions
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### Immediate (This Week)
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1. **Fix high-priority notebooks** (4 notebooks)
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- 02_mcmc_tutorial.ipynb
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- 03_differential_evolution_tutorial.ipynb
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- 04_real_world_applications.ipynb
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- 05_performance_benchmarks.ipynb
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2. **Document current API**
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- Create API reference showing correct parameter names
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- Add migration guide from old to new API
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### Short Term (Next Week)
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3. **Fix remaining notebooks** (2 notebooks)
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- 04_kalman_filter_sensor_fusion.ipynb (syntax cleanup)
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- mean_field_games_tutorial.ipynb
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4. **Add notebook CI/CD**
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```yaml
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# .github/workflows/notebooks.yml
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name: Validate Notebooks
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on: [push, pull_request]
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jobs:
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execute-notebooks:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/setup-python@v4
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- run: pip install jupyter nbconvert optimizr
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- run: |
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for nb in examples/notebooks/*.ipynb; do
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jupyter nbconvert --to notebook --execute "$nb"
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done
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```
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### Long Term (Month 2)
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5. **Automated notebook testing**
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- Integrate pytest-notebook
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- Run notebooks in CI on every commit
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- Block merges if notebooks fail
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6. **API stability policy**
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- Document breaking changes in CHANGELOG
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- Provide migration scripts for notebook updates
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- Version notebooks with library releases
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---
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## Next Steps
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1. **Investigate remaining failures**
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```bash
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# Read full error outputs
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cat /tmp/notebook_errors/*
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```
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2. **Check current API signatures**
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```python
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# In Python REPL
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import optimizr
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help(optimizr.mcmc_sample)
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help(optimizr.differential_evolution)
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```
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3. **Fix notebooks one by one**
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- Update API calls to match current library
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- Re-execute: `jupyter nbconvert --execute --inplace <notebook>.ipynb`
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- Commit with outputs
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4. **Setup CI for notebooks**
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- Add GitHub Actions workflow
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- Test on every push to main
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---
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## Impact Assessment
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### Documentation Quality
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- **Current**: 25% of tutorials work out of the box ❌
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- **Target**: 100% of tutorials execute cleanly ✅
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- **User Experience**: New users will hit errors immediately (critical issue)
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### Repository Credibility
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- **Current**: v1.0.0 with broken examples undermines release quality
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- **Risk**: Users may assume library itself is broken
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- **Urgency**: HIGH - should be fixed before major promotion
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### Mitigation
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- ✅ Committed working notebooks (2/8) to show some validation
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- ✅ Created transparent issue report (this document)
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- ⏳ Priority fixes in progress (high-value notebooks first)
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- ⏳ CI/CD to prevent future drift
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---
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**Report Generated**: 2026-02-16
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**Next Review**: After high-priority notebook fixes
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**Owner**: HFThot Research Lab
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**Repository**: https://github.com/ThotDjehuty/optimiz-r
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