b473f6a1ec
`_elmhurst_dwelling_type` derived a flat's roof exposure from
`room_in_roof is not None`, so a top-floor flat whose roof is a plain
external "PS Pitched, sloping ceiling" (no room-in-roof) fell through to
"Mid-floor flat". The cascade's `_dwelling_exposure` then treats a
mid-floor flat's roof as a party ceiling (RdSAP 10 §5 / §3 — party
surfaces carry no heat loss) and drops the entire roof term: cert
001431's 105 m² roof at U=2.3 = 241.68 W/K (30) vanished, collapsing
(33) fabric heat loss 320.06 → 78.38 and over-rating SAP by ~5 points
(on top of the age-band roof-U bug — see prior commit).
Read the roof TYPE instead — the dual of the floor's "Another dwelling
below" signal. A flat's roof is a party ceiling only when its Elmhurst
code is S / A / NR (Same/Another dwelling or Non-residential space
above); F / PN / PA / PS are exposed external roofs, so the dwelling is
on the top storey. `has_exposed_roof = room_in_roof present OR
_elmhurst_roof_is_exposed(roof)` — which is exactly what the function's
own docstring already described as the intent ("RR present or external
roof"), now implemented.
With both upstream fixes the full chain (Summary PDF → extractor →
mapper → cert_to_inputs → calculator) reproduces the worksheet's §11a
unrounded SAP 56.3649 at abs < 1e-4, with (30)/(33)/(37) matching to
the decimal. Only flat fixture reclassified; 000784 (top-floor, RR) and
000910 (ground-floor) unchanged. Regression suite green bar the 3
pre-existing unrelated fails.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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|---|---|---|
| .devcontainer | ||
| .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