25261d5c8b
346 corpus certs lodge roof_insulation_thickness="NI" (Not Indicated,
parsed to 0 by _parse_thickness_mm). When the description also signals
retrofit insulation ("Pitched, insulated (assumed)" / "Flat,
insulated" / "Roof room(s), insulated (assumed)"), our cascade
returned the uninsulated Table 16 row-0 value (U=2.30).
RdSAP 10 §5.11.4 (page 44, end of section): "If retrofit insulation
present of unknown thickness use 50 mm". That maps to Table 16 row
"Insulation at joists at ceiling level, 50 mm" = 0.68 W/m²K. The fix
is the analog of S-B27 for roofs: when insulation_thickness_mm==0
(the "NI" sentinel) and _described_as_insulated(description), return
0.68 instead of the row-0 lookup.
Per-cert delta: ΔU = 1.62 W/m²K on the affected slice; for typical
80 m² roof = 130 W/K HLC reduction ≈ 12 kWh/m² PEUI per cert.
Parity probe at 300 certs, seed=7:
SAP MAE 4.72 → 4.69 (-0.03) ← first SAP MAE drop in 3 slices
PE MAE 44.19 → 43.32 (-0.87)
PE bias 38.56 → 37.69 (-0.87)
Cumulative across S-B23 → S-B28:
PE MAE 57.28 → 43.32 (-13.96)
PE bias 51.56 → 37.69 (-13.87)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| 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_backlog.sh | ||
| 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