6884ec9fda
The gov-EPC API surfaces the assessor's RdSAP-assessed per-element U-values as `roof_u_value` / `wall_u_value` / `floor_u_value` on each building part. These were undeclared on the RdSAP 21.0.0/21.0.1 schemas, so `from_dict` silently dropped them, and `heat_transmission` re-derived each U from the §5.6 /§5.7/§5.11 construction-default cascade. The gov OPEN data routinely redacts the backing insulation thickness, so that re-derivation mis-bills an insulated element as uninsulated. RdSAP 10 §5.1: a known element U-value (documentary evidence / the lodged RdSAP output) is used directly in place of the construction-default cascade. Per [[project_per_cert_mapper_validation_state]] the gov API carries RdSAP OUTPUT, so the lodged U reproduces the official's element heat loss exactly. Worst case in the 2026 sample: cert 7921-0052-0940-5007-0663, an age-C "Pitched, sloping ceiling" (rc=8) top-floor flat lodging roof_u_value=0.2 with no thickness. The cascade returned the uninsulated 2.30 W/m²K → SAP 56.9 vs lodged 80 (-23.09, the single largest error in the sample). The roof override alone recovers ~15 SAP; the wall override (lodged 0.34 vs cascade) closes the rest of this cohort. Override applies to the MAIN wall only (alt-wall sub-areas keep their own per-area U) and the part's floor=0. Fires only when the rare field is present (9 of 909 computed certs), so the Summary path — which never lodges these API fields — is untouched. API gauge: 67.1% → 67.7% within-0.5, mean|err| 1.024 → 0.992. Worksheet harness: 47/47, 0 divergers (unchanged). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| 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