696d43112e
predicted_total_fuel_cost_gbp was silently mispricing every non-gas property because primary_main_fuel_type / water_heating_fuel store the gov EPC API enum (26=mains gas, 27=LPG, 28=oil, 29=electricity) and our _FUEL_UNIT_PRICE dict is keyed by Table 32 codes (1=gas, 4=oil, 30=elec). Codes 26-29 hit the dict's default 3.48 p/kWh -- silently treating electric immersion as gas. Concrete impact on OX1 5LR Sep 2025 cert (worst-predicted SAP=41, model 84): water_heating_fuel=29 (electric immersion). Real DHW cost 2941 kWh * 13.19p = £388/yr; we computed 2941 * 3.48 = £102 (4x under). Net predicted_total_fuel_cost £292 vs implied real £2513 -- predicted_ecf 0.49 (~SAP 93) vs real ECF 4.24 (SAP 41). Effect: every off-gas property's predicted_ecf was systematically too low, dragging the model's catastrophic-low-SAP predictions toward mid-band. Expected to substantially reduce decile-0 bias on retrain. New _API_TO_TABLE32 map covers codes 0-29. 4 new AAA tests; VERSION 2.2.0 -> 2.3.0 (MINOR; behavioural fix to existing column values). |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs/adr | ||
| 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