a64e857b94
A roof lodged "Unknown loft insulation" carries roof_insulation_thickness "NI" (Not Indicated → parsed to 0) or "ND" (None): the thickness is UNDETERMINED, not zero. RdSAP 10 §5.11.4 (p.44) is deterministic here — "U-values in Table 18 are used when thickness of insulation cannot be determined" — so the roof takes the Table 18 age-band default (column (1) pitched / column (3) flat), NOT the uninsulated 2.30 the Table 16 row-0 lookup returns for a parsed-0 thickness. The "Unknown" text is RdSAP's rendering of the undetermined-thickness observation, distinct from a genuine "no insulation" lodgement (which keeps 2.30). u_roof gains an "unknown"-description branch ahead of the parsed-0 → 2.30 path, gated on undetermined thickness (None or 0). Top-floor flats with "Pitched/Flat, Unknown ... insulation" were the worst electric-flat under-raters: roof U=2.30 gave HLP ~3.7 on dwellings rated SAP 69-70. Cluster (14 certs, roof desc contains "unknown", no "no insulation"): mean |err| 7.79 → 1.82, within-0.5 1→4, within-1.0 1→6. Cert 9836 roof_w_per_k 58.2→10.1, SAP -27.8 → -3.5. Eval headline 44.4% → 44.8%, mean |err| 1.944 → 1.851. Two certs overshoot (other residuals the wrong roof-U was masking); the spec value is applied uniformly regardless. 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