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