4d9dd9aaf3
For a dwelling with two main systems heating different parts, the (93)m Table 4e mean-internal-temperature adjustment was taken from system 1 alone (`details[0]`). SAP 10.2 p.186 requires the (203)-weighted mean of each system's control adjustment — (1 - (203)) for system 1, (203) for system 2 — mirroring the Table 9b responsiveness weighting already applied two lines above. Keyed to system 1, a dwelling led by a small-fraction system (e.g. a 20% HHRSH, adj 0.0 °C, ahead of an 80% manual-charge storage heater, adj +0.7 °C) was modelled ~0.7 °C too cool → space heat under-counted → over-rated. Compute the weighted adjustment at the call site. Shared `cert_to_inputs` path, so it also lifts the accredited gov-API RdSAP corpus (81.5% → 81.7%, guardrail). 5 Edmund Close +2.74 → +0.59; 4 Edmund Close -2.51 → -0.55. LRHA cohort within-0.5 60.2% → 62.1%, MAE 0.496 → 0.445; ratchets re-based to 0.62 / 0.45. Also corrects an oil-combi docstring (462051619031 is a Grant Vortex 10599, not the Worcester 18415). 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