c4b2782931
Per [[project-golden-coverage-state]] memory + docs/HANDOVER_GOLDEN_COVERAGE.md:
cohort-2 (38 certs) was chain-tested at 1e-4 SAP via
`test_summary_pdf_mapper_chain.py::test_api_cohort_2_full_chain_sap_
matches_worksheet_at_1e_minus_4` but NOT in golden — PE/CO2 cascade
output for 38 worksheet-backed certs had zero regression guards.
Largest invisible drift: cert 2102-3018-0205-7886-5204 at PE +20.36
kWh/m² / CO2 −0.79 t/yr. Was the +20.4 PE outlier called out in the
handover doc. Now pinned and visible to any future cascade refactor.
Cohort-2 SAP residuals all close to 0 at integer (1e-4 continuous-SAP
convergence rounds to exact match). PE / CO2 baseline residuals
captured at HEAD:
- PE residual range: −4.18 to +20.36 kWh/m². Median ≈ −0.1.
- CO2 residual range: −0.79 to +0.05 t/yr. Median ≈ −0.02.
- 14 certs cluster at PE ≈ −2.7 to −4.2 kWh/m² (gas combi PCDB
+ boiler PE under-count pattern, shared with cohort-1
cert 2130 and ASHP cohort certs).
Pinned per the existing `_GoldenExpectation` shape — SAP at abs=0
(integer), PE at abs=0.01 kWh/m², CO2 at abs=0.001 t/yr. Notes kept
short for each cohort-2 cert because the pinned residual itself is
the signal; per-cert slice history lives in the chain test's
`_COHORT_2_API_CLOSED` list and `sap worksheets/Additional data with
api/<cert>/dr87-*.pdf` worksheets.
Test suite: 317 pass (was 279) + 9 expected 000565 cascade-gap fails
(unchanged). Pyright: 1 error baseline preserved on the
`pytest.approx` import line (per [[feedback-prefer-abs-diff-over-
pytest-approx]] this is the legacy `test_api_to_domain_mapper_
preserves_main_heating_index_number` line that pre-dates the AAA
convention; not touched by this slice).
Next-investigation target: cert 2102 PE +20.36 / CO2 −0.79 closure.
Likely sits in the secondary-heating House coal PE/CO2 cascade
(S0380.43 closed SAP via spec-fuel routing but didn't address the
PE/CO2 paths). Visible as a fired golden test on any cascade refactor
of that surface.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| 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