171cb97c6e
First end-to-end test running EpcPropertyData → cert_to_inputs →
calculate_sap_from_inputs → SapResult and comparing against the
Elmhurst worksheet's headline SAP rating (line 258).
Current state:
000490 mid-terrace gas combi, time-clock keep-hot
SAP rating: 57 = 57 ✓ exact integer match
Continuous: 56.72 vs 57.40 → 0.7 points off (rounding noise)
000474 end-terrace gas combi, PCDB Vaillant ecoTEC pro
SAP rating: 55 vs 62 → 7 points UNDER
Space heating: 12299.6 vs 10612.9 (+16%)
Hot water: 3020.0 vs 2291.8 (+32%)
The 000474 gap localises to (a) the legacy hot-water cascade not
knowing about PCDB Table 3b combi loss (over-estimates HW by 32%) and
(b) likely a downstream space-heating-efficiency consequence. Both will
shrink once the §4 worksheet orchestrator + Table 3b are wired into
cert_to_inputs.
Tolerances set at the CURRENT gap so subsequent improvements show up
as tightening, not silent drift. The 000474 ceiling drops to ≤2 SAP
points once the worksheet §4 path lands in the mapper.
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