f3dcd7b43e
The Elmhurst dwelling-type classifier keyed "Top-floor flat" on a "dwelling
below" floor lodgement. A single-storey flat exposed BOTH top (a real
external roof) AND bottom (floor over partially-heated space, no dwelling
below) therefore fell through to "Ground-floor flat" — which the cascade's
_dwelling_exposure maps to has_exposed_roof=False, dropping the external
roof entirely.
Surfaced by simulated case 34 (cert 001431 reconfigured as a slimline
electric-storage flat): the worksheet bills (30) External roof = 39.98 m²
x U=2.30 = 91.95 W/K — the dominant heat-loss element — but the cascade
dropped it, under-stating space-heating demand by 42% (6550 vs 11357
kWh/yr) and over-predicting SAP by +21.76 (57.07 vs worksheet 35.31).
Fix: an exposed (non-party) roof puts the flat on the top storey
regardless of what is below it. Classify as "Top-floor flat" whenever the
roof is exposed; the flat's exposed floor is recovered downstream by the
existing per-BP is_above_partially_heated_space / is_exposed_floor override
in heat_transmission (§3). Party-roof flats ("another dwelling above") are
unaffected and stay Ground-/Mid-floor.
This is an Elmhurst-mapper (dwelling_type) bug, NOT a calculator bug: the
calculator correctly trusts dwelling_type, and the gov-API path supplies
the position directly (cert 0036 — a genuine ground-floor flat whose API
data lodges a "Pitched, no access" roof construction under another dwelling
— stays party, 2.51 W/K). API SAP gauge unchanged (57.6% within 0.5);
worksheet harness 47/47 unaffected; case 34 roof now exact (residual -1.61
is a separate flat-corridor wall-U thread). Regression gate green (3
pre-existing fails unrelated).
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