TreeGov — Risk Segmentation Workbench

Version: 1.0.0a1 Author: Marco Rengel License: Apache 2.0 Graphics engine: PySide6 Analytical engine: Python (NumPy, pandas, scipy.stats) Test suite: 199/199 passing

⚠️ Regulatory notice. TreeGov is under technical validation (Model Risk Management - MRM). Restricted use: technical MRM validation. Not fit for banking production without regulatory approval. Any production deployment requires independent validation, formal approval, and integration with the institution's model governance framework.

Executive summary

TreeGov is an analytical workbench for credit risk segmentation with native model governance (MRM) and IFRS 9 alignment. It implements the three classic decision-tree algorithms — CHAID (with its Exhaustive variant), C&RT, QUEST — with white-box mathematics: every statistical formula is written explicitly in the source code and cited to its original publication.

Unlike general-purpose libraries (scikit-learn, statsmodels, R packages) that require programming, TreeGov provides a graphical environment where the analyst focuses on analysis rather than code. Unlike discontinued GUI tools, it is open source, actively maintained, and built from the ground up with regulatory traceability (SR 11-7, Basel, IFRS 9) as a first-class design principle.

Philosophy

"Python gives you the engine. TreeGov gives you the engine, the brakes, the dashboard, the seatbelt, and the maintenance log. Which would you choose for a model that will be audited by the regulator?"

TreeGov starts from one premise: model governance is not a layer you add at the end, but a principle woven into every component. While statistical libraries and scripting environments provide the analytical engine, TreeGov provides in addition the brakes (MRM controls), the dashboard (graphical display), the seatbelt (auditability), and the maintenance log (the immutable audit trail). For a model that will be audited by a regulator, the choice is not technical: it is about governance.

Read the full project philosophy → English · Español

What is TreeGov?

An analytical segmentation tool for credit risk with native MRM/IFRS 9 governance. It implements CHAID (Kass, 1980), C&RT (Breiman et al., 1984) and QUEST (Loh & Shih, 1997) with auditable white-box mathematics.

Design principles

1. Clean-room architecture: strict separation between the analytical backend (backend_v3/, no GUI dependencies) and the presentation layer (frontend_v3/, no analytical logic).
2. White box: sklearn, statsmodels, and any other black-box library are forbidden. Every statistical formula is written explicitly in the code, with its bibliographic citation.
3. Native governance: DEBUG-grade transactional logging, immutable configuration (frozen SplitConfig), and mathematical invariant validation at construction time.

Analytical capabilities

Technology stack

• Python 3.11 / 3.12
• PySide6 (graphical interface)
• numpy, pandas, scipy.stats (statistical computation)
• pyarrow, openpyxl (data ingestion)
• pytest (optional: tests are runnable with plain python)

Repository structure

TreeGov/
├── backend_v3/              # Pure analytical layer (0 Qt imports)
│   ├── import_guard.py      # Black-box library blocker
│   ├── log_config.py        # DEBUG transactional logger
│   ├── types.py             # SplitConfig (frozen) + enums
│   ├── statistics.py        # chi2, F, Brown-Forsythe, Stirling, Kass
│   ├── imputation.py        # TrainOnlyImputer (anti-leakage)
│   ├── chaid_engine.py      # CHAID classic + Exhaustive
│   ├── crt_engine.py        # C&RT binary + surrogate splits
│   ├── quest_engine.py      # QUEST binary + univariate QDA
│   ├── splitting.py         # Hamilton-Hare stratified split
│   ├── profiling.py         # Predictor type inference
│   ├── tree_builder.py      # BFS tree construction + TreeModel
│   ├── session_state.py     # Flow state machine (EMPTY to TRAINED to OOT)
│   ├── validation.py        # OOS metrics + OOT evaluation
│   ├── pruning.py           # Cost-complexity pruning (Breiman)
│   ├── reporting.py         # Gains table + classification matrix DTOs
│   └── stability.py         # PSI / CSI / Gini Drop
├── frontend_v3/             # Presentation layer (Phase 2)
├── tests/                   # Unit tests (dual: pytest / python direct)
├── docs/                    # Documentation
│   ├── methodology.md       # Mathematical methodology (EN)
│   ├── api.md               # Public API (EN)
│   ├── roadmap.md           # Phase plan (EN)
│   ├── philosophy.md        # Project philosophy (EN)
│   ├── philosophy.es.md     # Philosophy (ES)
│   ├── estado_proyecto.md   # Project state dossier (ES)
│   ├── audit_log.md         # Audit trail (ES)
│   ├── contexto_para_nueva_sesion.md  # Session bootstrap (ES)
│   └── prompt_para_asistente.md       # Assistant brief (ES)
├── archive/                 # Historical documents (read-only)
├── data/                    # Datasets (read-only, Git-ignored)
├── logs/                    # Per-run execution logs (Git-ignored)
├── treegov.py               # Headless orchestrator (CLI)
├── treegov_gui.py           # GUI entry point (Phase 2)
├── pyproject.toml           # Packaging
├── README.md                # This file (EN)
├── README.es.md             # Spanish translation
├── AGENTS.md                # Operating instructions for AI agents
├── CONVENTIONS.md           # Operational conventions for Aider
├── LICENSE                  # Apache 2.0
├── .gitignore               # Git exclusions
├── .aiderignore             # Aider repo-map exclusions
└── .treegovrules            # Governance rules for AI agents

Quick start

Installation

.\.venv\Scripts\python -m pip install -r requirements.txt

Headless demo (Phase 0)

.\.venv\Scripts\python treegov.py

Performance benchmark (N=1M)

.\.venv\Scripts\python tests\benchmark_1m.py --n 1000000 --seed 42

Test suite

.\.venv\Scripts\python -m pytest tests -q

Project status

Test suite: 199/199 passing.

Academic references

• Kass, G.V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics 29(2), 119-127.
• Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
• Loh, W.-Y. & Shih, Y.-S. (1997). Split Selection Methods for Classification Trees. Statistica Sinica 7(4), 815-840.
• Cochran, W.G. (1954). Some Methods for Strengthening the Common Chi-2 Tests. Biometrics 10(4), 417-451.
• Biggs, D., de Ville, B. & Suen, E. (1991). A Method of Choosing Multiway Partitions for Classification and Decision Trees. Journal of Applied Statistics 18(1), 49-62.
• Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Wiley.

Regulatory references

• Basel (BCBS CRE 20.39): regulatory PD floors.
• EBA/GL/2017/16: guidelines on the specification, estimation, and validation of PD/LGD parameters in IRB models.
• IFRS 9: staging, ECL, and asymmetric threshold optimization.
• SR 11-7 / OCC 2011-12: model risk management (MRM) guidance.

License

Apache License 2.0 — see LICENSE.

Contact

Marco Rengel — marcrengel@gmail.com — github.com/Marco-Rengel

Translations

• Español (Spanish)