# Verification Checklist

## Before Running
- [ ] Install dependencies: `pip install -r requirements.txt`
- [ ] Ensure CUAD dataset is at: `dataset/CUAD_v1/CUAD_v1.json`
- [ ] Python 3.8+ installed

## Tests to Run

### 1. Basic Comparison (4 methods)
```bash
python3 compare_risk_discovery.py
```

**Expected:**
- K-Means ✅
- LDA ✅
- Hierarchical ✅
- DBSCAN ✅
- Output files created
- No KeyError
- No TypeError

### 2. Advanced Comparison (9 methods)
```bash
python3 compare_risk_discovery.py --advanced
```

**Expected:**
- All 4 basic methods ✅
- NMF ✅ (no alpha parameter error)
- Spectral ✅
- GMM ✅
- Mini-Batch K-Means ✅
- Risk-o-meter ✅
- Output files created

### 3. Limited Dataset
```bash
python3 compare_risk_discovery.py --max-clauses 1000
```

**Expected:**
- Runs faster
- Uses 1000 clauses max
- All methods complete

### 4. Custom Data Path
```bash
python3 compare_risk_discovery.py --data-path dataset/CUAD_v1/CUAD_v1.json
```

**Expected:**
- Loads from specified path
- All methods complete

## Output Files to Check
After successful run:
- [ ] `risk_discovery_comparison_report.txt` exists
- [ ] `risk_discovery_comparison_results.json` exists
- [ ] Report contains all methods
- [ ] JSON is valid and parseable

## Key Metrics to Verify
In the report, check for:
- [ ] Each method has `Patterns Discovered` count
- [ ] Execution times are reasonable
- [ ] Quality metrics are present (silhouette/perplexity)
- [ ] Top patterns are displayed
- [ ] Recommendations section is complete

## Common Issues and Solutions

### Issue: No module named 'sklearn'
**Solution:** `pip install scikit-learn>=1.3.0`

### Issue: No module named 'gensim' (Risk-o-meter only)
**Solution:** `pip install gensim>=4.3.0` or skip with basic mode

### Issue: Dataset not found
**Solution:** Check path in `--data-path` argument or use default location

### Issue: Out of memory
**Solution:** Use `--max-clauses 5000` to limit dataset size

### Issue: Slow execution
**Solution:** 
- Use basic mode (without `--advanced`)
- Reduce `--max-clauses`
- Skip Spectral/Hierarchical for large datasets

## Performance Expectations

For ~13K clauses (full CUAD):
- K-Means: ~10-30 seconds ⚡
- LDA: ~30-60 seconds 🟡
- Hierarchical: ~60-120 seconds 🟡 (memory intensive)
- DBSCAN: ~20-40 seconds ⚡
- NMF: ~15-45 seconds ⚡
- Spectral: ~90-180 seconds 🔴 (slow for large datasets)
- GMM: ~40-80 seconds 🟡
- Mini-Batch K-Means: ~5-15 seconds ⚡⚡
- Risk-o-meter: ~60-120 seconds 🟡

**Total time (advanced mode):** ~6-12 minutes

## Success Criteria
✅ All methods complete without errors
✅ Output files generated
✅ Report contains meaningful patterns
✅ Quality metrics are calculated
✅ No KeyError or TypeError exceptions