doc/LDA_FIX_GET_LABELS.md

🔧 LDA Integration Fix - get_risk_labels() Method

❌ Problem Identified

AttributeError: 'TopicModelingRiskDiscovery' object has no attribute 'get_topic_labels'

Root Cause: The LDARiskDiscovery.get_risk_labels() method was calling self.lda_backend.get_topic_labels(), but this method doesn't exist in the TopicModelingRiskDiscovery class.

---

✅ Solution Applied

File: risk_discovery.py

Before (Broken):

def get_risk_labels(self, clause_texts: List[str]) -> List[int]:
    if self.cluster_labels is None:
        raise ValueError("Must discover patterns first")
    
    # ❌ This method doesn't exist!
    labels = self.lda_backend.get_topic_labels(clause_texts)
    return labels

After (Fixed):

def get_risk_labels(self, clause_texts: List[str]) -> List[int]:
    if self.cluster_labels is None:
        raise ValueError("Must discover patterns first")
    
    # ✅ Implement directly using LDA model
    cleaned_texts = [self.lda_backend._clean_text(text) for text in clause_texts]
    feature_matrix = self.lda_backend.vectorizer.transform(cleaned_texts)
    doc_topic_dist = self.lda_backend.lda_model.transform(feature_matrix)
    
    # Return the topic with highest probability
    labels = doc_topic_dist.argmax(axis=1).tolist()
    return labels

---

🔧 Additional Fix: train.py Return Values

File: train.py

Problem: When errors occurred, main() returned None, causing:

TypeError: cannot unpack non-iterable NoneType object

Fixed: Changed all return statements in error handlers to return None, None

Before:

except Exception as e:
    print(f"❌ Error: {e}")
    return  # ❌ Returns None

After:

except Exception as e:
    print(f"❌ Error: {e}")
    return None, None  # ✅ Returns tuple

Also updated the main call:

if __name__ == "__main__":
    result = main()
    if result is not None:
        trainer, history = result
    else:
        print("\n❌ Training failed.")
        exit(1)

---

🎯 How It Works Now

1. Pattern Discovery:

lda = LDARiskDiscovery(n_clusters=7)
results = lda.discover_risk_patterns(train_clauses)
# Discovers 7 topics using LDA

2. Getting Labels for New Clauses:

labels = lda.get_risk_labels(new_clauses)
# Returns: [2, 0, 5, 1, ...]  (dominant topic per clause)

Process:

1. Clean text using _clean_text()
2. Transform to document-term matrix using vectorizer.transform()
3. Get topic probabilities using lda_model.transform()
4. Return argmax() - the topic with highest probability

3. Getting Full Probability Distribution (Optional):

dist = lda.get_topic_distribution(new_clauses)
# Returns: [[0.1, 0.05, 0.7, ...], ...]  (probabilities for each topic)

---

✅ Verification

Test Script: test_lda_get_labels.py

The test script verifies:

1. ✅ LDARiskDiscovery instance creation
2. ✅ Pattern discovery works
3. ✅ get_risk_labels() returns correct format
4. ✅ Labels are integers in valid range
5. ✅ get_topic_distribution() returns probability matrix

Run test:

python3 test_lda_get_labels.py

---

🚀 Ready to Use

The fix is complete! You can now run:

python3 train.py

Expected output:

🎯 Using LDA (Topic Modeling) for risk discovery
🔍 Discovering risk patterns using LDA (n_topics=7)...
  📊 Creating document-term matrix...
  🧠 Fitting LDA model...
✅ LDA discovery complete: 7 risk topics found

🔍 Discovered Risk Patterns:
  • Topic_PARTY_AGREEMENT
    Keywords: party, agreement, shall, company, consent
  • Topic_INTELLECTUAL_PROPERTY
    Keywords: shall, product, products, agreement, section
  ...

---

📊 Technical Details

Method Signature:

def get_risk_labels(self, clause_texts: List[str]) -> List[int]

Returns:

• List of integers representing dominant topic IDs
• Range: 0 to (n_clusters-1)
• Length: Same as input clause_texts

Algorithm:

1. Text Cleaning: Remove extra whitespace, normalize
2. Vectorization: Convert to bag-of-words using CountVectorizer
3. LDA Transform: Get document-topic probability distribution
4. Argmax: Select topic with highest probability per document

Example:

Input:  ["The party shall indemnify...", "Governed by state law..."]
Probs:  [[0.1, 0.05, 0.7, 0.1, 0.05], [0.2, 0.6, 0.1, 0.05, 0.05]]
Output: [2, 1]  # Topics 2 and 1 have highest probabilities

---

🎯 Key Differences from K-Means

Aspect	K-Means	LDA
Assignment	kmeans.predict()	lda_model.transform() + argmax()
Hard/Soft	Hard clusters	Soft topics (with probabilities)
Model	Centroid-based	Probabilistic topic model
Output	Cluster ID	Dominant topic ID + full distribution

---

🐛 Troubleshooting

Error: "No module named 'sklearn'"

pip install scikit-learn

Error: "Must discover patterns first"

Solution: Call discover_risk_patterns() before get_risk_labels():

lda.discover_risk_patterns(train_clauses)  # First
labels = lda.get_risk_labels(test_clauses)  # Then

Error: "Feature names are different"

Solution: You must use the same clauses to discover patterns that will be used for training. The LDA model learns vocabulary from the training set.

---

✅ Status

• Fixed get_risk_labels() method implementation
• Fixed train.py return values for error handling
• Created test script for verification
• Documented the fix
• Ready for production use

You can now train with LDA! 🎉

---

Files Modified:

1. risk_discovery.py - Fixed get_risk_labels() (lines 350-373)
2. train.py - Fixed return statements (lines 66, 89, 152)
3. test_lda_get_labels.py - New test script (created)
4. doc/LDA_FIX_GET_LABELS.md - This documentation (created)

Status: ✅ FIXED AND READY