f4a8d2a017
Pins 7 certs from a 1000-cert random sample that satisfy:
|SAP rounded-int residual| ≤ 1
|PE residual| ≤ 10 kWh/m²
main_heating_category != 4 OR main_heating_data_source != 1
(non-PCDB-heat-pump — PCDB lookup is deferred)
Cert mix: 6 cat=2 gas/oil boilers (3 PCDB, 3 Table 4b) + 1 cat=6 heat
network. Age bands A, C, D (×3), F, J, L. TFAs 75-526. Mix of
detached / semi-detached / mid-terrace / mid-floor flat. The cleanest
PE match in the set (cert 7536-3827) has PE residual -0.29 kWh/m².
Purpose: regression anchor. Future slices that improve aggregate MAE
silently break individual certs unless caught here. Each cert's
expected residual is recorded in `_EXPECTATIONS` so the diff is
human-inspectable when a regression fires.
The set is acknowledged to contain compensating-errors cases: some
certs match SAP within ±1 because the cert-calibration prices absorb
multiple structural deviations from spec. Hand-trace of 7536-3827
showed PE matched (-0.29) but cost was £143 (12%) under cert's implied
cost — a multi-factor gap (price calibration + missing gas standing
charge + lighting over-prediction) that cancels back into SAP ±1. We
accept this with the tolerance choice: tightening to PE ±5 in our
sample would have yielded zero fixtures.
Tolerance can tighten over the session as we close the PE bias
(currently +38 kWh/m² systematic).
All 301 domain tests pass; no behaviour changed.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .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