06b4ef3d12
RdSAP "first floor over passageway" rule — when an upper storey has
larger floor area than the storey immediately below, the excess
overhangs an unheated space or external air and routes through
Table 20's U_exposed_floor (1.20 W/m²K for age-D + no insulation,
the modal cohort lodging).
Cohort ground-truth: cert 2636 BP0 floor 1 (42.92 m²) − floor 0
(39.18 m²) = 3.74 m². Worksheet (28b) "Exposed floor Main: 3.74 ×
1.20 = 4.4880" matches the spec rule exactly.
`_part_geometry` now computes `cantilever_floor_area_m2` per BP.
The per-BP loop in `heat_transmission_from_cert` injects U×A onto
the floor accumulator and includes the area in (31) total external
area (which feeds (36) thermal bridges).
Gated to avoid false positives on flats and sub-ground multi-storey
shapes:
- `property_type == "0"` (house) — excludes flats (cert 9501 BP0
has 6.85 m² floor 0 + 74.43 m² floor 1; the diff is stairwell
access, not a real cantilever).
- `excess >= 1 m²` — excludes 2-dp rounding artefacts (cert 001479
Main BP0 lodges floor 1 = 30.77 vs floor 0 = 30.45 → 0.32 m²
drift that's not a real cantilever; would otherwise add 0.4
W/K and break the closed-cert 1e-4 Layer 4 chain gate).
- `excess / prev_area < 0.25` — excludes sub-ground / partial-
storey shapes (cert 7536 BP0: 33.7/17.28 = 195% — not a real
cantilever; floor 0 likely a partial vestibule, not the full
ground footprint).
Cohort impact: cert 2636 SAP residual closes from +0.4873 → -0.0055
(by far the largest cohort outlier becomes the closest match).
Zero regressions: 654 pass + 10 pre-existing baseline fails (9 cert
001479 hand-built skeleton + 1 FEE). All 7 ASHP certs now cluster
within ±0.06 SAP vs worksheet.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| scripts | ||
| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
| Dockerfile.test.dockerignore | ||
| Makefile | ||
| MEMORY.md | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| test.requirements.txt | ||
| tox.ini | ||
| UBIQUITOUS_LANGUAGE.md | ||
Model Repository
This repository contains the code pertaining to the development of the data science and machine learning products being utilised by Hestia.
The different folders in this repository relate to services that can be used independently, or can be imported and used as part of a larger application
Getting Started
Prerequisites
Dev Container Setup
This repo uses a Docker Compose-based dev container. The model-backend service joins a shared-dev Docker network so it can communicate with other local services (e.g. a frontend container) running on your machine.
VS Code users: The initializeCommand in devcontainer.json creates the shared-dev network automatically before the container starts. No manual step required — just open the repo and select Reopen in Container.
Non-VS Code / CI workflows: Run the following once before starting the container:
make dev-setup
This is idempotent and safe to re-run if the network already exists.
Folders
backend/
This folder contains the code for the fastapi backend service, which provides an interface to much of the functionality in this repository, for the frontend
model_data/
This folder contains related to the reading and preparation of assessment model data, including pulling out epc attributes
Testing
All tests can be run, against the configuration in pytest.ini running
pytest
This will run the complete panel of tests and report on coverage in the locations specified by the pytest.ini file.
To run tests in a specific service, e.g. inside of model_data, simply run
pytest --cov-config=model_data/.coveragerc --cov=model_data
This will produce the test results and coverage reports