86eff23f08
User reframed the end goal explicitly: the production flow is `API JSON → EpcPropertyDataMapper.from_api_response → SAP calculator` landing within ±0.5 of the API-published SAP. The Elmhurst-site-notes work is the cross-validation route — same dwelling, independent path into EpcPropertyData. Once both routes agree on cert 001479, the API mapper is validated by transitivity. Restructure the handover around four nested validation layers: Layer 1 (hand-built cascade pin): 6 cohort certs GREEN; 001479 partial Layer 2 (Elmhurst ≡ hand-built): cohort 000474 GREEN; 5 others pending Layer 3 (API ≡ Elmhurst): test doesn't exist yet Layer 4 (API cascade ±0.5): 72.08 vs 69 (delta +3.08) Each layer validates the one below. Closing inner-most first means upper layers can lean on it as reference. Documents tools/patterns built in slices 63-70: - `_LOAD_BEARING_FIELDS` allow-list (~40 cascade/semantic fields) - `_NON_LOAD_BEARING_WINDOW_SUBFIELDS` deny-list (descriptive int/str encoding noise) - `_diff_load_bearing` recursive helper (strict-pyright-clean) - `test_from_elmhurst_site_notes_matches_hand_built_NNNNNN` tracer- bullet pattern (000474 is the worked example) Next-step ordering: parametrize over 5 other cohort certs, complete 001479 hand-built (currently 2/11 cascade pins green; gap −3.02 SAP), add cert 001479 to diff test, then add API mapper → hand-built diff test, then the production-flow acceptance pin in test_golden_fixtures for cert 001479. Lists source-data caveats (the M-vs-L Ext1 age discrepancy on 001479). Conventions to honour (AAA, abs(diff)<=tol, one slice=one commit, 1e-4 Elmhurst / 0.5 API, no widening, pyright net-zero). Cached artefacts (golden JSON, Summary PDF, worksheet PDF) noted. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|---|---|---|
| .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