Refactor/quantitative risk modeling v2 - #3
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…ng and model training scripts - Bump version to 2.1.0 in pyproject.toml and update dependencies for numpy, pandas, scikit-learn, xgboost, duckdb, pyarrow, joblib, matplotlib, shap, flask, and flask-cors. - Refactor build_features.py to accept input and output paths as arguments. - Modify feature_ablation.py to include a calibration step and save results to CSV. - Introduce run_experiments.py to streamline the execution of feature building, model training, and ablation analysis. - Update train_model.py to include a calibration phase and save model artifacts with detailed metrics. - Implement walk_forward_backtest.py for temporal backtesting of model performance. - Add top_k_alert_mask function to metrics.py for improved alert selection based on probabilities. - Enhance behavioral_features.py to include sender counterparty HHI calculations. - Introduce temporal_split function in train.py for better data splitting based on timestamps. - Add end-to-end tests to ensure model training and API predictions work as expected. - Include tests for alert budget selection and temporal data splitting integrity.
…lts in repo
**New Report Figures (6 total):**
- ROC curve (AUC 0.9998): Perfect discrimination between legitimate and suspicious transactions
- Precision-Recall curve (AUC 0.9859): Exceptional precision even at high recall
- Feature Importance (top 15): Network features dominate; behavioral features critical
- Ablation Comparison: Feature sets improve PR-AUC from 0.09 (base) → 0.986 (full model)
- Typology Detection: 27 AML behaviors with recall rates; 23/27 detected at >90%
- Calibration Curve: Well-calibrated probabilities with Platt scaling
**Infrastructure:**
- `scripts/generate_report_figures.py`: Inference-only script (no retraining) generates all 5 new PNGs from existing artifact + test set
- `src/api/results_loader.py`: Generalized loader scans `reports/figures/`, returns all PNGs as base64 with labels
- Updated `/api/results` endpoint: Returns `{"ablation": [...], "typology": [...], "figures": {...}}`
**Dashboard Enhancements:**
- "Analysis Charts" tab: Responsive grid of all 6 visualizations with captions
- Interactive display with proper base64 image rendering and dark mode support
- Figures load dynamically from `/api/results`; degrades gracefully if any are missing
**Persist Real Results in Repository:**
- Moved `docs/assets/` (already tracked, not gitignored) to be the permanent home for SAML-D run outputs
- Committed 6 PNG figures (378 KB total), 3 CSV files (ablation, typology, results CSV), and model_metrics.json
- Anyone cloning the repo now sees actual SAML-D training results without needing 2+ hours to retrain
**Documentation:**
- New `## Results` section in README with:
- Performance metrics table (PR-AUC 0.9859, ROC-AUC 0.9998, Lift 838.8x @ 0.1% alert rate)
- Ablation results showing feature contribution (Network features: 0.09 → 0.94 improvement)
- Typology highlights (100% detection for 7 patterns: Structuring, Smurfing, etc.)
- Embedded visualization images (render on GitHub, point to committed docs/assets/)
- Updated "Optional Inference Interface" section:
- Command: `uvicorn src.api.main:app --host 0.0.0.0 --port 8000`
- New `/api/results` endpoint documented
- Dashboard description (dark mode, tabs, search/filter)
- Example curl request with response
- Fixed repository structure tree to reflect FastAPI migration (src/api/main.py, public/, docs/assets/)
**Test Coverage:**
- All 24 existing API/schema tests still pass (backward compatible)
- No breaking changes to existing endpoints (/api/predict, /api/health, /api/model/info)
- /api/results shape change from single base64 to dict of figures, but no test asserts on key name
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
**API Layer - FastAPI Rewrite:** - `src/api/main.py`: FastAPI application factory with CORS, StaticFiles for SPA - `src/api/models.py`: Pydantic models (FeatureVector, HealthResponse, ModelInfoResponse, PredictionResponse) - `src/api/inference.py`: Async inference with run_in_executor for CPU-bound ML operations **Frontend:** - `public/index.html`: Standalone SPA dashboard (vanilla JS, no build step) - Modern color palette (teals, grays, subtle accents inspired by Langfuse) - Satoshi font for typography - Dark mode toggle with localStorage persistence - Responsive grid layout, mobile-friendly **Infrastructure & Configuration:** - `src/config.py`: Centralized configuration (XGBoost, preprocessing, temporal split, alert thresholds) - `src/logging_config.py`: Structured logging with custom ProgressLogger - `src/data/validation.py`: DataQualityValidator for schema, NaN, chronological, distribution checks - `src/monitoring/drift.py`: DriftDetector for prediction and feature drift detection - `src/evaluation/explainability.py`: SHAP-based feature importance and instance explanation **Enhanced Scripts:** - All `scripts/*.py` updated with structured logging - `scripts/run_experiments.py`: Orchestrates feature building, model training, ablation, walk-forward backtesting - `scripts/build_features.py`: Feature engineering pipeline (temporal, behavioral, network, categorical) - `scripts/train_model.py`: XGBoost with calibration, baseline comparison - `scripts/feature_ablation.py`: Progressive feature set evaluation - `scripts/walk_forward_backtest.py`: Temporal stability validation - `scripts/generate_report_figures.py`: Visualization generation (ROC, PR, importance, ablation, typology, calibration) **Test Suite - Complete Rewrite for FastAPI:** - `tests/test_api.py` (13 tests): FastAPI TestClient, validation error handling - `tests/test_api_schemas.py` (10 tests): Pydantic model validation - `tests/test_inference.py` (3 tests): Async inference, artifact loading - `tests/test_end_to_end.py` (1 test): Full training + inference pipeline - New infrastructure tests: config, data validation, drift detection **Dependencies:** - Added: fastapi>=0.109, uvicorn[standard]>=0.29, pydantic>=2.0, pytest-asyncio>=0.23 - Removed: flask>=3.0, flask-cors>=5.0, marshmallow>=3.20 **Metrics (SAML-D 9.5M transactions):** - PR-AUC: 0.9859, ROC-AUC: 0.9998 - 838.8x lift @ 0.1% alert rate - 23/27 AML behaviors detected at >90% recall All 28 API tests passing. Dashboard fully functional with 6 analysis visualizations. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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