42e0bb3122
The §5.16 Table 22 thermal-mass-parameter (TMP) "always low-mass" set was
{timber 5, cob 7, park home 8}. But wall_construction code 8 is OVERLOADED by
the same gov-API/calc code-space divergence as the wall-U fix: the Summary
path's "PH" mapping uses 8 for park home, while the gov-EPC API enum uses 8
for SYSTEM BUILD (Summary system build = code 6). So every API system-built
cert was mis-rated as low-mass 100 kJ/m²K instead of masonry 250 (Table 22
lists system build as masonry — PDF p.48, line "System build 250...").
A too-low TMP shortens the §7 time constant tau = Cm/(3.6·H), over-cutting
the temperature reduction so mean internal temperature is UNDER-stated →
space-heating demand under-stated → SAP over-rated. This was the cause of the
uninsulated system-built over-rate cluster (n=9 gas-boiler certs at signed
+2.39 vs cavity +0.43 / solid-brick +0.08 at the same bands — a system-built-
specific anomaly with a spec-correct wall U).
Fix: drop 8 from the always-low set and gate it on `property_type` — code 8 is
the low-mass park-home value only when the dwelling really is a park home,
otherwise it is gov-API system build and keeps masonry 250. Disambiguated by
the same `property_type == "park home"` signal used elsewhere in the cascade.
Worksheet harness UNAFFECTED (47/47, 0 divergers): the Summary path uses code
6 for system build and code 8 only for genuine park homes (which stay
low-mass via the property_type gate). API gauge 65.3% -> 67.1% within-0.5
(mean|err| 1.059 -> 1.024, signed +0.050 -> -0.002). The uninsulated
system-built cluster collapses +2.82 -> +0.28 signed (0/11 -> 7/11 within
0.5). 2 AAA tests (parametrised code-8 system-built -> 250; park-home
property -> 100). pyright net-zero.
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