26e0584855
Two outputs of validating the Wythenshawe cohort against accredited Elmhurst: 1. Harness note: the DB-oracle residual tail is not engine defects. The two worst under-raters, 6 Barry Road (ours 60.12, oracle 62) and 87 Alderue (ours 61.80, oracle 63), were re-keyed into accredited Elmhurst on our exact inputs — Elmhurst returned 60 and 62, matching our engine and both below the lodged oracle. So our engine computes correct RdSAP; the lodged certs carried a lighter input set (dropped the surveyed 17.4 m2 solid-brick alternative wall, +1.7 SAP). It's input-provenance drift, not a calculation gap — don't chase these as engine bugs or tune the engine to close them. Corrects the earlier "spec-literal alt-wall" note. 2. epc-to-elmhurst skill gotcha: give Elmhurst the RAW surveyed room height, not the engine's storey height. The mapper already adds +0.25 m to upper floors (_UPPER_FLOOR_HEIGHT_ADD_M), so feeding sap_floor_dimensions.room_height_m makes Elmhurst double-add the joist void (~1 SAP too low — it under-scored 6 Barry 60->59 until the first-floor height was corrected 2.76 -> raw 2.51). Comment/doc only; no behaviour change (harness still 89.2%/0.297, pyright clean). 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