c89206fc7f
`_ELMHURST_PARTY_WALL_CODE_TO_SAP10` only recognised the bare "C" and "S" leading codes. Cert 001479 Main §7 lodges "Party Wall Type: CU Cavity masonry unfilled" — the leading token is "CU", which fell through to None and made `u_party_wall` apply the unknown-default U=0.25 instead of the worksheet's lodged U=0.50. Add "CU" → 4 (SAP10 WALL_CAVITY); `u_party_wall(4) = 0.5 W/m²K` matches the worksheet's §3 `Party walls Main … 0.50` row exactly. This widens the chain residual on cert 001479 (cascade SAP 63.17 → 61.90 vs target 69.0094) — not a regression: pre-slice the cascade was UNDER-counting party-wall heat loss (U=0.25 vs the lodged 0.50), which masked over-counting elsewhere. The party-wall U-value is now worksheet-accurate; remaining 7.1 SAP gap will narrow as the other mapper gaps (Ext2 exposed floor, roof insulation thickness, secondary heating SAP code, etc.) land in follow-up slices. All 10 chain tests green (6 cohort + 2 cert-001479 structural pins). Pyright net-zero (35-error baseline preserved on mapper.py). 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