email-sorter/scripts/test_ml_only.sh
FSSCoding 53174a34eb Organize project structure and add MVP features
Project Reorganization:
- Created docs/ directory and moved all documentation
- Created scripts/ directory for shell scripts
- Created scripts/experimental/ for research scripts
- Updated .gitignore for new structure
- Updated README.md with MVP status and new structure

New Features:
- Category verification system (verify_model_categories)
- --verify-categories flag for mailbox compatibility check
- --no-llm-fallback flag for pure ML classification
- Trained model saved in src/models/calibrated/

Threshold Optimization:
- Reduced default threshold from 0.75 to 0.55
- Updated all category thresholds to 0.55
- Reduces LLM fallback rate by 40% (35% -> 21%)

Documentation:
- SYSTEM_FLOW.html - Complete system architecture
- VERIFY_CATEGORIES_FEATURE.html - Feature documentation
- LABEL_TRAINING_PHASE_DETAIL.html - Calibration breakdown
- FAST_ML_ONLY_WORKFLOW.html - Pure ML guide
- PROJECT_STATUS_AND_NEXT_STEPS.html - Roadmap
- ROOT_CAUSE_ANALYSIS.md - Bug fixes

MVP Status:
- 10k emails in 4 minutes, 72.7% accuracy, 0 LLM calls
- LLM-driven category discovery working
- Embedding-based transfer learning confirmed
- All model paths verified and working
2025-10-25 14:46:58 +11:00

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#!/bin/bash
# Test ML performance without LLM fallback using trained model
set -e
echo "=========================================="
echo "ML-ONLY TEST (No LLM Fallback)"
echo "=========================================="
echo ""
echo "Using model: src/models/calibrated/classifier.pkl"
echo "Testing on: 1000 emails"
echo ""
# Activate venv
if [ -z "$VIRTUAL_ENV" ]; then
source venv/bin/activate
fi
# Run classification with trained model, NO LLM fallback
python -m src.cli run \
--source enron \
--limit 1000 \
--output ml_only_test/ \
--no-llm-fallback \
2>&1 | tee ml_only_test.log
echo ""
echo "=========================================="
echo "Test complete. Check ml_only_test.log"
echo "=========================================="