85fe51b623
A mid-terrace / enclosed-mid-terrace (built_form 4/6) has external walls on
its two opposite (front/rear) faces only (RdSAP 10 §1.1, p.7-8); both end/
gable elevations — and their room-in-roof continuations — abut a neighbour
and are party walls (§1.4.3, p.9). The gov-API path is built_form-blind, so a
cert lodging an "Exposed" RR gable (`gable_wall_type=1`) on such a form had it
billed at the masonry main-wall U (~2.5) instead of the party 0.25 (Table 4,
p.22), over-counting fabric heat loss and under-rating the dwelling.
`_api_type_1_gable_kind` now takes `built_form` and reclassifies an Exposed RR
gable to Party on the both-ends-party forms {4, 6} only; end-terrace {3, 5},
semi {2} and detached {1} keep their genuine exposed gable. The API path lodges
no per-gable U, so this only ever overrides a cascade fallback.
Corroborated three ways: RdSAP §1.1 + Table 4; S10TP-05 Appendix A (a
mid-terrace's roof-gable junction length is zero); and three accredited
Elmhurst mid-terrace worksheets (000477/000480/000516), which all lodge Party
gables. Those worksheets run the site-notes path (already correct) so the 195
Elmhurst pins are untouched.
Corpus (RdSAP-21.0.1, 1000 certs): within-0.5 78.8% -> 79.5%, SAP MAE
0.625 -> 0.599, PE MAE 2.85 -> 2.71, CO2 MAE 0.070 -> 0.068. Cert
100040550095 -10.13 -> +0.37; the 18-cert terraced-external-gable cohort mean
-1.94 -> +0.11; end-terrace/semi/detached untouched. Thresholds ratcheted.
Tests: new test_mapper_rir_gable_terraced (9); 195 Elmhurst pins, 288
cert_to_inputs/real-cert pins, 54 mapper tests, and the corpus gauge all green;
no new pyright errors.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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| .claude/skills | ||
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| 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 | ||
| modelling_audit.md | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| 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