361f91546b
Two related bugs both produced U=1.7 for retrofit-insulated solid-brick
walls when the spec says U=0.55 (Table 6 footnote: "If a wall is known
to have additional insulation but the insulation thickness is unknown,
use the row in the table for 50 mm insulation"):
1. _insulation_bucket(0, True) returned 0 instead of 50. The "NI"
sentinel parses to 0 via _parse_thickness_mm, then the bucket
function's "< 25 -> 0" branch ignored the insulation_present signal.
Affects 56 corpus certs lodging solid-brick with type=1 or type=3
plus thickness="NI".
2. wall_ins_present was set False whenever wall_insulation_type == 4
("as-built / assumed"), even if the description said
"...insulated (assumed)" or "...partial insulation (assumed)".
Affects 128+51 = 179 corpus certs.
The same root pattern as S-B25 (cavity-wall description disambiguation),
extended to non-cavity constructions. `_cavity_described_as_filled`
generalised to `_described_as_insulated`; now used by:
- u_wall (cavity-wall dispatcher to the Filled-cavity row, S-B23/B25)
- heat_transmission_from_cert (override wall_ins_present for non-cavity
walls so the 50 mm bucket routes per Table 6 footnote)
Parity probe at 300 certs, seed=7:
PE MAE 45.74 → 45.37 (-0.37)
PE bias 40.19 → 39.75 (-0.44)
Band D bias +42.7 → +41.6 (-1.1)
Band F bias +12.6 → +10.7 (-1.9)
Modest aggregate movement — the affected population is small (~0.6% of
corpus, ~2 certs in the 300 sample). The slice's correctness is proved
by 4 unit tests in test_rdsap_uvalues.py + 2 end-to-end tests in
test_heat_transmission.py.
Cumulative across S-B23 → S-B26:
PE MAE 57.28 → 45.37 (-11.91)
PE bias 51.56 → 39.75 (-11.81)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|---|---|---|
| .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