4c50c1b0fb
Per user guidance: trust the cert's lodged meter_type as the source of
truth for tariff selection, rather than inferring tariff from heating
code lists. SAP10 meter_type enum (verified empirically on the 250k
corpus: 75% type 2, 14% type 1, 11% type 3):
1 = Off-peak (Economy-7 / dual rate)
2 = Single (Standard)
3 = Off-peak (24-hour heating)
The transform.py docstring describes 1=Standard / 2=Off-peak but that
contradicts the 75% type-2 distribution (UK demographics don't put 75%
of dwellings on off-peak). The inverted reading parity-tests correctly.
Tariff routing rules:
- Space heating: off-peak rate when main fuel is electric AND meter is
off-peak; else standard main-fuel rate.
- Hot water: off-peak rate when water fuel is electric AND meter is
off-peak; else water-fuel rate.
- Lighting + pumps + fans: always standard electricity (Table 12a
notwithstanding — cert software empirically uses standard here).
100-cert parity probe:
MAE 4.40 → 4.39 (flat in aggregate; structurally cleaner code)
RMSE 5.63 → 5.56
bias +0.16 → -0.17
within ±10: 96% (unchanged)
The meter_type seam replaces the e7_eligible_main_codes set on
PriceTable. Conceptually cleaner: tariff is a property of the meter,
not the heating system.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|---|---|---|
| .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