db3bf00602
A dwelling's heating is one conceptual system, but its fields are scattered across EpcPropertyData (a gov-API schema mirror): the cluster on sap_heating, the electricity tariff on sap_energy_source.meter_type, hot-water flags loose at top level. Three places synthesise a heating system — Measure Options, Landlord Overrides, EPC Prediction's donor — and each hand-copied a different ad-hoc subset. The override and donor both dropped meter_type, so an electric-storage system landed on the template's single-rate meter and billed overnight heat at the peak rate: property 713406 scored SAP 13 (G) vs ~50 (E), inflating the HHRSH measure to +45.8 and overshooting the plan to band A. Establish a single Coherent Heating System boundary (CONTEXT.md) that every synthesiser must cover, with a source-appropriate fill policy (ADR-0035): - Override overlay *completes* the partial system the landlord named. Companion fields are now DERIVED from the SAP code, not hand-attached per archetype: the off-peak meter from the calculator's single off-peak classification (new OFF_PEAK_IMPLYING_HEATING_CODES = SAP §12 Rules 1-2), and an unobserved storage charge control defaults to the conservative manual control (Table 4e 2401). So adding a heating archetype is just adding its code — companions can't be forgotten. A contract test guards it (every off-peak code drags a Dual meter). - Prediction's heating donor now *carries* the donor's meter_type alongside its sap_heating cluster — the donor is already coherent. Coherence is a synthesis-time obligation only; the calculator still scores a real lodged cert exactly as lodged. Verified on 713406: baseline 13 -> 47.8 (E), matching its recorded rating; the phantom HHRSH recommendation is gone and the plan no longer overshoots to A. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
||
|---|---|---|
| .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 | ||
| 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