CLUSTERING_EXECUTION_LOG.md

ToGMAL Enhanced Clustering - Execution Log

Date: October 18, 2025
Status: In Progress
Goal: Upgrade from TF-IDF to Sentence Transformers for better cluster separation

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Setup Complete ✅

Dependencies Installed

✓ sentence-transformers==5.1.1
✓ datasets==4.2.0
✓ scikit-learn (already installed)
✓ matplotlib==3.10.7
✓ seaborn==0.13.2
✓ torch==2.2.2
✓ transformers==4.57.1
✓ numpy==1.26.4 (downgraded from 2.x for compatibility)

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Step 1: Dataset Fetching ✅

Script: enhanced_dataset_fetcher.py

Datasets Fetched

GOOD Cluster (LLMs Excel - >80% accuracy)

Dataset	Source	Samples	Domain	Performance
squad_general_qa	rajpurkar/squad_v2	500	general_qa	86%
hellaswag_commonsense	Rowan/hellaswag	500	commonsense	95%
TOTAL		1000

LIMITATIONS Cluster (LLMs Struggle - <70% accuracy)

Dataset	Source	Samples	Domain	Performance
medical_qa	GBaker/MedQA-USMLE-4-options	500	medicine	65%
code_defects	code_x_glue_cc_defect_detection	500	coding	~60%
TOTAL		1000

HARMFUL Cluster (Safety Benchmarks)

Dataset	Source	Samples	Status
toxic_chat	lmsys/toxic-chat	0	⚠️ Config error (need to specify 'toxicchat0124')

Note: Math dataset (hendrycks/competition_math) failed to load - will add alternative later

Cache Location

/Users/hetalksinmaths/togmal/data/datasets/
├── squad_general_qa.json (500 entries)
├── hellaswag_commonsense.json (500 entries)
├── medical_qa.json (500 entries)
├── code_defects.json (500 entries)
└── combined_dataset.json (2000 entries total)

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Step 2: Enhanced Clustering (In Progress) 🔄

Script: enhanced_clustering_trainer.py

Configuration

• Embedding Model: all-MiniLM-L6-v2 (sentence transformers)
• Clustering Method: K-Means
• Number of Clusters: 3 (targeting: good, limitations, harmful)
• Total Samples: 2000
• Batch Size: 32

Progress

[1/4] Generating embeddings... (in progress)
├─ Model downloaded: all-MiniLM-L6-v2 (90.9MB)
├─ Progress: ~29% (18/63 batches)
└─ Estimated time: 1-2 minutes remaining

[2/4] Standardizing embeddings... (pending)
[3/4] K-Means clustering... (pending)
[4/4] Cluster analysis... (pending)

Expected Output

1. Clustering Results:

   • Silhouette score (target: >0.4, vs current TF-IDF 0.25)
   • Davies-Bouldin score (lower is better)
   • Cluster assignments for each sample

2. Cluster Analysis:

   • Category distribution per cluster
   • Domain distribution per cluster
   • Purity scores (% of primary category)
   • Dangerous cluster identification (>70% limitations/harmful)

3. Pattern Extraction:

   • Keywords per cluster
   • Detection heuristics
   • Representative examples

4. Export to ToGMAL:

   • ./data/ml_discovered_tools.json (for dynamic tools)
   • ./models/clustering/kmeans_model.pkl (trained model)
   • ./models/clustering/embeddings.npy (cached embeddings)

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Expected Results

Hypothesis

With sentence transformers, we expect:

Cluster 0: GOOD (general QA + commonsense)

• Primary categories: 100% "good"
• Domains: general_qa, commonsense
• Keywords: question, answer, what, context
• Purity: >90%
• Dangerous: NO

Cluster 1: LIMITATIONS - Medicine (medical QA)

• Primary categories: ~100% "limitations"
• Domains: medicine
• Keywords: diagnosis, patient, treatment, symptom
• Purity: >85%
• Dangerous: YES → Will generate check_medical_advice tool

Cluster 2: LIMITATIONS - Coding (code defects)

• Primary categories: ~100% "limitations"
• Domains: coding
• Keywords: function, code, bug, vulnerability
• Purity: >85%
• Dangerous: YES → Will generate check_code_security tool

Comparison to Baseline

Metric	TF-IDF (Baseline)	Sentence Transformers (Target)
Silhouette Score	0.25-0.26	>0.4 (54-60% improvement)
Cluster Purity	~71-100%	>85% (more consistent)
Cluster Separation	Moderate	High (semantic understanding)
Dangerous Clusters Identified	2-3	2 (cleaner boundaries)

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Next Steps (After Clustering Completes)

1. ✅ Verify Results

   • Check silhouette score improvement
   • Review cluster assignments
   • Validate dangerous cluster identification

2. ✅ Export to Dynamic Tools

   • Confirm ./data/ml_discovered_tools.json generated
   • Verify format matches ml_tools.py expectations

3. ✅ Test Integration

   # Test ML tools loading
   python -c "from togmal.ml_tools import get_ml_discovered_tools; import asyncio; print(asyncio.run(get_ml_discovered_tools()))"
   

4. ✅ Visualization

   • Generate 2D PCA projection of clusters
   • Compare with TF-IDF clustering visually

5. 📝 Update Documentation

   • Add results to CLUSTERING_TO_DYNAMIC_TOOLS_STRATEGY.md
   • Update requirements.txt with new dependencies

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Issues Encountered

1. NumPy Version Incompatibility ✅ FIXED

Error: PyTorch compiled with NumPy 1.x, but NumPy 2.x installed
Solution: Downgraded to numpy<2 (1.26.4)

2. HuggingFace Dataset Loading

Issue: Some datasets require specific configs

• lmsys/toxic-chat needs config: 'toxicchat0124' or 'toxicchat1123'
• hendrycks/competition_math not accessible (may be private)

Workaround:

• Using 2000 samples (1000 good, 1000 limitations) is sufficient for proof-of-concept
• Can add more datasets later (see CLUSTERING_TO_DYNAMIC_TOOLS_STRATEGY.md for alternatives)

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File Artifacts Created

/Users/hetalksinmaths/togmal/
├── enhanced_dataset_fetcher.py (354 lines) ✅
├── enhanced_clustering_trainer.py (476 lines) ✅
├── CLUSTERING_TO_DYNAMIC_TOOLS_STRATEGY.md (628 lines) ✅
├── CLUSTERING_EXECUTION_LOG.md (THIS FILE)
│
├── data/
│   ├── datasets/
│   │   ├── combined_dataset.json ✅
│   │   └── *.json (individual dataset caches) ✅
│   │
│   ├── ml_discovered_tools.json (TO BE GENERATED)
│   └── training_results.json (TO BE GENERATED)
│
└── models/
    └── clustering/
        ├── kmeans_model.pkl (TO BE GENERATED)
        └── embeddings.npy (TO BE GENERATED)

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Timeline

• 15:00-15:15: Dependencies installation
• 15:15-15:25: Dataset fetching (completed)
• 15:25-15:35: Embedding generation (in progress)
• 15:35-15:40: Clustering & analysis (pending)
• 15:40-15:45: Export to ML tools (pending)

Estimated completion: 15:40-15:45 SGT

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Success Criteria

• Datasets fetched (2000 samples minimum)
• Sentence transformers embeddings generated
• Silhouette score >0.4 (vs 0.25 baseline)
• 2+ dangerous clusters identified
• ML tools cache exported
• Integration with existing togmal_list_tools_dynamic verified

Status: 60% complete