aff331ff34
Replace the empirical `_elmhurst_has_suspended_timber_floor` heuristic
(which keyed on Room-in-Roof < Main ground area) with the mechanical
RdSAP 10 Specification §5 rule (page 29):
- Age band A-E: U-value < 0.5 → sealed (0.1); retro insulation + no
U → sealed (0.1); otherwise unsealed (0.2)
- Age band F-M: sealed (0.1)
- Park home: unsealed (0.2)
- Only applies when Main bp's lowest floor is a "Ground floor" with
"Suspended timber" construction
The spec rule is derived in `_has_suspended_timber_floor_per_spec`
(cert_to_inputs.py) and applied in `ventilation_from_cert` whenever
the lodged `epc.sap_ventilation.has_suspended_timber_floor` is None.
Explicit lodged values (cohort hand-built fixtures) take precedence.
Impact on cert 001479 (the load-bearing API↔Elmhurst parity-test
fixture; previously the RR-based heuristic returned False for this
no-RR semi-detached, dropping (12) entirely):
Mapper → cascade → SAP delta vs worksheet 69.0094:
BEFORE: +1.1903 (mapper extracted False; cascade applied (12)=0)
AFTER : +0.2290 (mapper extracts None; spec derives True/unsealed;
cascade applies (12)=0.2 → matches worksheet)
Cohort cascade pins remain GREEN (66 of 66) — cohort hand-built
fixtures retain their explicit `has_suspended_timber_floor` values
which override the spec derivation.
Expected cohort regressions to triage in the next slice:
- 4 cohort chain tests RED (000474, 000480, 000487, 000490) — their
Elmhurst worksheets enter non-spec (12) values (0.0 or 0.2 when
spec predicts the opposite) so the mapper-path cascade now
diverges from the worksheet PDF at 1e-4.
- 6 cohort diff tests RED — mapper now produces
has_suspended_timber_floor=None while the cohort hand-builts
retain explicit True/False overrides, producing a 1-field
divergence per cohort cert.
Pyright net-zero (mapper 35→35; cert_to_inputs 35→35) — dead
`_elmhurst_has_suspended_timber_floor` removed.
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