a7761ea83f
The gov-API `main_fuel_type`/`water_heating_fuel` enum (epc_codes.csv) codes 30="waste combustion (community)", 31="biomass (community)", 32="biogas (community)" collide in VALUE with the Table-32 electricity codes 30 (standard rate), 31 (7-hour low) and 32 (7-hour high). All three sit in `_ELECTRIC_FUEL_CODES`, so `is_electric_fuel_code` flagged a community-scheme main as electric and `_is_electric_main` routed its cost through the off-peak electricity branch — BYPASSING the heat-network rate in `_heat_network_factor_fuel_code`. Cert 8536 (biomass community, SAP code 301) was billing at 5.5 p/kWh grid electricity instead of the 4.24 p/kWh heat-network rate → -17.2 SAP. Per RdSAP 10 §C / SAP 10.2 Table 12 (PDF p.191) the community waste/biomass/biogas rows are codes 42/43/44 (the same rows the backwards-compat enum codes 11/12/13 already map to). Add 30->42, 31->43, 32->44 to both API fuel-translation tables. The remap CANNOT be global (`canonical_fuel_code`): the cascade uses the bare Table-32 code 30 internally as `_STANDARD_ELECTRICITY_FUEL_CODE` (the RdSAP no-water-heating immersion default writes `water_heating_fuel=30`), so a blanket remap mis-prices genuine grid electricity as community waste (cert 2211 regressed +16 SAP in a prototype). Instead `_heat_network_community_fuel_code` translates only when `_is_heat_network_main` is true, at the `_main_fuel_code` / `_water_heating_fuel_code` fuel-TYPE boundary, where the community meaning is unambiguous. Per the strict-raise principle ([[reference-unmapped-sap-code]]), a heat-network main lodging a colliding community fuel the table doesn't cover raises `UnmappedSapCode` rather than silently falling through to the same-numbered electricity code. Eval (API SAP vs lodged): cert 8536 -17.25 -> -6.51, cert 5036 -6.29 -> +1.36; mean|err| 1.329 -> 1.312, within-1.0 67.88% -> 67.99%, within-2.0 81.74% -> 81.85%, within-0.5 held at 53.14%, 909 computed / 0 raises. No golden / calculator regressions. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
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| sap worksheets | ||
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| 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