doc/LDA_FIX_EXTRACT_FEATURES.md

# 🔧 LDA Integration Fix #2 - extract_risk_features() Method

## ❌ Problem Identified

```python
AttributeError: 'LDARiskDiscovery' object has no attribute 'extract_risk_features'
```

**Root Cause:** The trainer calls `self.risk_discovery.extract_risk_features()` to generate synthetic severity and importance scores, but `LDARiskDiscovery` was missing this method and its required attributes.

---

## ✅ Solution Applied

### **Added to `LDARiskDiscovery` class:**

#### **1. Legal Pattern Attributes**

```python
# Legal language patterns
self.legal_indicators = {
    'obligation_strength': r'\b(?:shall|must|required|...)\b',
    'prohibition_terms': r'\b(?:shall not|must not|...)\b',
    'conditional_risk': r'\b(?:if|unless|provided|...)\b',
    'liability_terms': r'\b(?:liable|responsibility|...)\b',
    'temporal_urgency': r'\b(?:immediately|within|...)\b',
    'monetary_terms': r'\$|USD|dollar|payment|...',
    'parties': r'\b(?:Party|Parties|Company|...)\b',
    'dates': r'\b(?:January|February|...)\b'
}

# Legal complexity indicators
self.complexity_indicators = {
    'modal_verbs': r'\b(?:shall|must|may|...)\b',
    'conditional_terms': r'\b(?:if|unless|...)\b',
    'legal_conjunctions': r'\b(?:whereas|therefore|...)\b',
    'obligation_terms': r'\b(?:agrees?|undertakes?|...)\b'
}
```

#### **2. clean_clause_text() Method**

```python
def clean_clause_text(self, text: str) -> str:
    """Clean and normalize clause text"""
    if not isinstance(text, str):
        return ""
    
    # Remove excessive whitespace
    text = re.sub(r'\s+', ' ', text)
    
    # Remove special characters but keep legal punctuation
    text = re.sub(r'[^\w\s.,;:()"-]', ' ', text)
    
    return text.strip()
```

#### **3. extract_risk_features() Method** ⭐

```python
def extract_risk_features(self, clause_text: str) -> Dict[str, float]:
    """Extract numerical features that indicate risk levels"""
    text_lower = clause_text.lower()
    words = text_lower.split()
    features = {}
    
    # Basic statistics
    features['clause_length'] = len(words)
    features['sentence_count'] = len(re.split(r'[.!?]+', clause_text))
    features['avg_word_length'] = np.mean([len(w) for w in words])
    
    # Legal language intensity (8 indicators)
    for pattern_name, pattern in self.legal_indicators.items():
        matches = len(re.findall(pattern, text_lower))
        features[f'{pattern_name}_count'] = matches
        features[f'{pattern_name}_density'] = matches / len(words)
    
    # Legal complexity (4 indicators)
    for pattern_name, pattern in self.complexity_indicators.items():
        matches = len(re.findall(pattern, text_lower))
        features[f'{pattern_name}_complexity'] = matches / len(words)
    
    # Composite risk scores
    features['obligation_strength'] = (
        features['obligation_strength_density'] * 2 +
        features['modal_verbs_complexity']
    )
    
    features['legal_complexity'] = (
        features['conditional_terms_complexity'] +
        features['legal_conjunctions_complexity'] +
        features['obligation_terms_complexity']
    )
    
    features['risk_intensity'] = (
        features['liability_terms_density'] * 2 +
        features['prohibition_terms_density'] +
        features['conditional_risk_density']
    )
    
    return features
```

---

## 🎯 How Trainer Uses These Features

### **In `trainer.py`:**

```python
def _generate_synthetic_scores(self, clauses, score_type):
    """Generate synthetic severity/importance scores"""
    scores = []
    
    for clause in clauses:
        # Extract risk features
        features = self.risk_discovery.extract_risk_features(clause)
        
        if score_type == 'severity':
            # Calculate severity from features
            score = (
                features['risk_intensity'] * 30 +
                features['obligation_strength'] * 20 +
                features['prohibition_terms_density'] * 100 +
                features['liability_terms_density'] * 100 +
                min(features['monetary_terms_count'] * 0.5, 2)
            )
        else:  # importance
            score = (
                features['legal_complexity'] * 30 +
                features['clause_length'] / 100 * 10 +
                features['obligation_strength'] * 20
            )
        
        scores.append(min(score, 10.0))
    
    return scores
```

---

## 📊 Feature Categories

### **1. Basic Statistics (3 features)**
- `clause_length` - Number of words
- `sentence_count` - Number of sentences
- `avg_word_length` - Average word length

### **2. Legal Indicators (16 features - 8 pairs)**
- `obligation_strength_count` / `obligation_strength_density`
- `prohibition_terms_count` / `prohibition_terms_density`
- `conditional_risk_count` / `conditional_risk_density`
- `liability_terms_count` / `liability_terms_density`
- `temporal_urgency_count` / `temporal_urgency_density`
- `monetary_terms_count` / `monetary_terms_density`
- `parties_count` / `parties_density`
- `dates_count` / `dates_density`

### **3. Complexity Indicators (4 features)**
- `modal_verbs_complexity`
- `conditional_terms_complexity`
- `legal_conjunctions_complexity`
- `obligation_terms_complexity`

### **4. Composite Scores (3 features)**
- `obligation_strength` - Weighted obligation indicator
- `legal_complexity` - Combined complexity score
- `risk_intensity` - Overall risk level

**Total:** ~26 features per clause

---

## ✅ Complete Interface

`LDARiskDiscovery` now has full compatibility with `UnsupervisedRiskDiscovery`:

### **Attributes:**
- ✅ `n_clusters`
- ✅ `discovered_patterns`
- ✅ `cluster_labels`
- ✅ `feature_matrix`
- ✅ `legal_indicators`
- ✅ `complexity_indicators`

### **Methods:**
- ✅ `discover_risk_patterns(clauses)` - Main discovery method
- ✅ `get_risk_labels(clauses)` - Get dominant topics
- ✅ `get_discovered_risk_names()` - Get topic names
- ✅ `get_topic_distribution(clauses)` - Get probabilities
- ✅ `clean_clause_text(text)` - Text cleaning
- ✅ `extract_risk_features(text)` - Feature extraction ⭐

---

## 🧪 Verification

### **Test Script: `test_lda_complete.py`**

Comprehensive test covering:
1. ✅ All required attributes
2. ✅ All required methods
3. ✅ Feature extraction functionality
4. ✅ Feature types and values
5. ✅ Text cleaning
6. ✅ Label assignment
7. ✅ Probability distributions

**Run test:**
```bash
python3 test_lda_complete.py
```

**Expected output:**
```
✅ Step 1: Import successful
✅ Step 2: Creating LDARiskDiscovery instance...
✅ Step 3: Checking required attributes...
✅ Step 4: Checking required methods...
✅ Step 5: Testing discover_risk_patterns()...
✅ Step 6: Testing extract_risk_features()...
   📊 Sample features:
      - risk_intensity: 0.143
      - obligation_strength: 0.167
      - legal_complexity: 0.000
✅ Step 7: Testing clean_clause_text()...
✅ Step 8: Testing get_risk_labels()...
✅ Step 9: Testing get_topic_distribution()...
✅ Step 10: Testing get_discovered_risk_names()...

🎉 ALL TESTS PASSED!
```

---

## 🚀 Ready to Train

Now you can run training without errors:

```bash
python3 train.py
```

**Expected flow:**
```
1. Load data ✅
2. Discover risk patterns using LDA ✅
3. Extract risk features for each clause ✅
4. Generate synthetic severity scores ✅
5. Generate synthetic importance scores ✅
6. Create training datasets ✅
7. Train model ✅
```

---

## 📝 Summary of Fixes

### **Fix #1: get_risk_labels()** (Previous fix)
- Implemented topic label extraction
- Used `argmax()` on probability distributions

### **Fix #2: extract_risk_features()** (This fix) ⭐
- Added `legal_indicators` dictionary (8 patterns)
- Added `complexity_indicators` dictionary (4 patterns)
- Implemented `clean_clause_text()` method
- Implemented `extract_risk_features()` method (26+ features)

---

## 🎯 Key Points

1. **Full Compatibility** - `LDARiskDiscovery` now matches `UnsupervisedRiskDiscovery` interface
2. **Feature-Rich** - Extracts 26+ numerical features per clause
3. **Domain-Agnostic** - Uses general legal language patterns
4. **Trainer-Ready** - Works seamlessly with synthetic score generation
5. **Tested** - Comprehensive test suite validates all functionality

---

## 📚 Files Modified

1. **`risk_discovery.py`** - Added methods and attributes
   - Lines 319-344: Added legal indicators
   - Lines 420-446: Added `clean_clause_text()`
   - Lines 448-496: Added `extract_risk_features()`

2. **`test_lda_complete.py`** - New comprehensive test (150 lines)

3. **`doc/LDA_FIX_EXTRACT_FEATURES.md`** - This documentation

---

## ✅ Status

- [x] Added legal_indicators dictionary
- [x] Added complexity_indicators dictionary
- [x] Implemented clean_clause_text() method
- [x] Implemented extract_risk_features() method
- [x] Created comprehensive test script
- [x] Documented all changes
- [x] **READY FOR TRAINING** 🎉

---

**Next:** Run `python3 train.py` to train with LDA! 🚀