6d9ef1143c
Two certs fetched fresh from the GOV.UK EPB register, each with an
Elmhurst Summary PDF (input) and a dr87 worksheet PDF (the (1)..(286)
ground truth):
0340-2467-9260-2006-6521 (Summary_000922 / dr87-0001-000922)
5500-5070-0822-0201-3663 (Summary_000920 / dr87-0001-000920)
Both run through BOTH front-ends — from_api_response and
from_elmhurst_site_notes — and through the rating + demand cascades.
Cross-mapper parity holds: the two paths agree to <1e-4 on continuous
SAP, fuel cost, CO2 and PE. Both paths reproduce the worksheet exactly:
0340: (255) cost 776.4295, (272) CO2 2875.0498, (286) PE 16474.5616;
fabric (33) 171.6188, (37) 205.9358; SAP int 70 = lodged.
5500: (255) cost 751.8295, (272) CO2 2423.4547, (286) PE 14397.0118;
fabric (33) 141.1226, (37) 167.3696; SAP int 66 = lodged.
Pinned in two tables of test_golden_fixtures.py:
- _EXPECTATIONS / test_golden_cert_residual_matches_pin — SAP/PE/CO2
residual vs the integer-rounded lodged register (SAP resid +0 both).
- _WORKSHEET_PE_CO2 / test_golden_cert_pe_co2_matches_worksheet —
PE (286)/(4) and CO2 (272) vs the worksheet at +0.0000 (the
load-bearing 1e-4 check; lodged register is integer-rounded).
Dropped-field audit (raw JSON keys vs the schema-21.0.1 dataclass
fields consumed by from_dict) re-run on both fresh JSONs: no new
silently-dropped fields — only created_at metadata and the
shower_outlet_type/shower_wwhrs keys already handled by
_normalize_shower_outlets (mapper.py:2047). No calculator or mapper
change required; this is pure validation + regression-pinning.
Full §4 suite: 2392 passed, 1 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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|---|---|---|
| .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