1f49fa03cd
The cert's `floor_insulation_thickness` field carries "NI" (Not
Indicated) on 58% of corpus certs — by far the most common value. For
~2 413 of those (12% of corpus) the description also says "Solid,
insulated (assumed)" or "Suspended, insulated (assumed)" — the
assessor saw insulation but didn't measure the thickness. Our
`_parse_thickness_mm("NI")` returns 0, which feeds `u_floor` as an
explicit "0 mm" → r_f=0 → uninsulated-floor U-value. Wrong.
RdSAP 10 §5.12 Table 19 footnote (2) (page 46): "For floors which
have retrofitted insulation, use the greater of 50 mm and the
thickness according to the age band". `u_floor` now accepts a
`description` kwarg; when `_described_as_insulated(description)` is
true and the lodged thickness is missing/zero, ins_mm =
max(50, age-band default).
Geometry sanity-check, 100 m² × 40 m perimeter, w=0.3 (B=5):
- Uninsulated solid floor: d_t = 0.615, U = 0.60 W/m²K
- 50 mm assumption: d_t = 2.758, U = 0.31 W/m²K
Parity probe at 300 certs, seed=7:
PE MAE 45.37 → 44.19 (-1.18)
PE bias 39.75 → 38.56 (-1.19)
Band J bias +41.2 → +29.7 (-11.5)
Band K bias +34.1 → +22.4 (-11.7)
Band L bias +19.6 → +11.3 (-8.3)
Band M bias +86.3 → +55.1 (-31.2)
Bands A-H mostly unchanged (max(50, 0) = 50 either way; description
overrides on older stock are rarer in this sample)
The K-L-M dwellings improved most because for them the age-band
default insulation (100-140 mm) is now applied instead of 0 mm.
Cumulative across S-B23 → S-B27:
PE MAE 57.28 → 44.19 (-13.09)
PE bias 51.56 → 38.56 (-13.00)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| 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_backlog.sh | ||
| 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