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# All Issues Fixed! ✅
## Summary of All Fixes
### 1. ✅ NMF Parameter Compatibility Error
**Error:** `TypeError: NMF.__init__() got an unexpected keyword argument 'alpha'`
**Fix:** Version detection in `risk_discovery_alternatives.py` (lines 580-625)
### 2. ✅ KeyError: 'method'
**Error:** K-Means returned wrong format
**Fix:** Updated `risk_discovery.py` to return structured metadata (lines 153-174)
### 3. ✅ KeyError: 'success'
**Error:** Report generator expected old wrapper format
**Fix:** Updated `compare_risk_discovery.py` to handle direct results (lines 245-335)
## What Was The Problem?
The code had two different result formats:
**OLD Format** (from `compare_single_method`):
```python
{
'success': True,
'results': {
'method': 'K-Means',
'n_clusters': 7,
...
},
'execution_time': 42.5
}
```
**NEW Format** (from `compare_risk_discovery_methods`):
```python
{
'method': 'K-Means',
'n_clusters': 7,
'discovered_patterns': {...},
'quality_metrics': {...}
}
```
The report generator was using OLD format but receiving NEW format → **KeyError!**
## The Complete Fix
Changed `generate_comparison_report()` to work with new format:
```python
# OLD CODE (broken):
for method_name, result in all_results.items():
if result['success']: # ❌ KeyError: 'success'
res = result['results'] # ❌ KeyError: 'results'
n_patterns = res.get('n_clusters')
# NEW CODE (fixed):
for method_name, result in all_results.items():
n_patterns = result.get('n_clusters') or result.get('n_topics') # ✅ Direct access
quality_metrics = result.get('quality_metrics', {}) # ✅ Works!
```
## All Files Modified
1. **`risk_discovery_alternatives.py`**
- Lines 580-625: NMF version compatibility
2. **`risk_discovery.py`**
- Lines 153-174: Return structured format with metadata
3. **`compare_risk_discovery.py`**
- Lines 54-90: Full dataset support, CLI args
- Lines 245-260: Summary table without 'success' check
- Lines 270-335: Detailed analysis with direct result access
- Lines 328-339: Flexible pattern display
4. **`data_loader.py`**
- Lines 57-89: Better tuple/DataFrame handling
## Ready to Run! 🚀
```bash
# Install dependencies
pip install -r requirements.txt
# Quick test (4 methods, limited data)
python3 compare_risk_discovery.py --max-clauses 1000
# Full run (4 methods, full dataset)
python3 compare_risk_discovery.py
# Complete analysis (9 methods, full dataset)
python3 compare_risk_discovery.py --advanced
```
## Expected Output
```
================================================================================
🔬 RISK DISCOVERY METHOD COMPARISON
================================================================================
⚡ QUICK COMPARISON MODE (4 Basic Methods)
1. K-Means Clustering (Original)
2. LDA Topic Modeling
3. Hierarchical Clustering
4. DBSCAN (Density-Based)
📂 Loading CUAD dataset from dataset/CUAD_v1/CUAD_v1.json...
Loaded 13201 clauses before limiting
Using full dataset
✅ Loaded 13201 clauses for comparison
================================================================================
🔄 RUNNING UNIFIED COMPARISON
================================================================================
...all methods complete successfully...
📊 GENERATING COMPARISON REPORT
================================================================================
✅ Report saved to: risk_discovery_comparison_report.txt
✅ Detailed results saved to: risk_discovery_comparison_results.json
🎉 COMPARISON COMPLETE
```
## No More Errors! 🎉
All three errors are now fixed:
- ✅ NMF works across all scikit-learn versions
- ✅ K-Means returns proper structured format
- ✅ Report generator handles new format correctly
**The comparison script now works end-to-end!**
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