3e2509fb7d
Teaches the pashub `from_site_notes` mapper the electric-storage-heater and room-heater families, following the existing boiler/heat-network split exactly (fix at the mapper boundary, ADR-0015 — never mis-rate, never strict-raise deep in the calculator). Adds: - Table 4e Group 4 storage charge-control codes (2401/2402/2404) and Group 6 room-heater control codes (2602/2605), each gated on its own system group so a control label never takes a wrong-group 2xxx code. - The Table 4a system code from the surveyed heater type lodged in the "Heating System (Other)" field (`community_heat_source`): Fan storage 404, slimline 402, PCDF-Search product ⇒ HHR 409 (RdSAP 10 p.80), room heaters 691/694. - A 2404⇔409 coherence guard: high-heat-retention controls on a non-HHR storage type is a surveyor contradiction and fails loud rather than rating a mismatch. All codes taken from the SAP 10.2 spec (14-03-2025 PDF) and already present in the calculator's inventories — no calculator change. LRHA WAVE 3 (lincs rural) accuracy: 8 computed / 96 blocked → 45 computed / 59 blocked / 0 errored (within-0.5 46.7%, MAE 2.989); ratchets re-baselined to the new coverage. Guinness cohort (208) + gov-API corpus unchanged; pyright --strict adds zero new errors. Closes #1619 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