19235d1144
A coal main (gov-API main_fuel_type=33) was priced at the electricity 10-hour low rate (7.5 p) and anthracite (5) at the bulk-LPG rate (12.19 p), because the shared price/CO2/PE lookups check Table-32/12-code membership BEFORE translating the API enum — and codes 5/33 collide with a different-fuel Table code. This drove the cohort's single worst cert (2100 anthracite, -61 SAP). `is_electric_fuel_code(33)` also wrongly classified the coal main as electric. The gov-API fuel enum (confirmed by description-vs-code audit on main_heating[].description): 5=anthracite, 33=coal, 9=dual-fuel, 20/25/31=community. The collision can't be resolved inside the shared table functions — code 33 is ALSO the electricity-10h TARIFF code used by the dual-rate CO2/PE split (golden 000565), so normalising there breaks electricity certs. Instead `canonical_fuel_code` normalises the colliding SOLID-fuel enums (5->15 anthracite, 33->11 house coal) at the fuel-TYPE boundary in `_main_fuel_code` / `_water_heating_fuel_code`, where the code is known to be a fuel type (never a tariff code). Scoped to anthracite (5) + coal (33) — the unambiguous large mispricings. Dual-fuel (9, 0.45 p delta) and community (20/25/31, heat-network path) are deferred (noted in `_GOV_API_COLLISION_FUELS`). API SAP eval: mean|err| 1.424 -> 1.329 (the -61 anthracite outlier 2100 -> -11, residual now fabric); within-0.5 53.1% (flat); 909 computed, 0 raises. Golden + Elmhurst regression green (the shared table functions are unchanged, so the electricity-tariff CO2/PE path is untouched). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
||
|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| 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 | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| 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