c380377948
RdSAP 10 §5.11.4: a pitched roof lodging thickness "NI" with description "...insulated (assumed)" was billed at the §5.11.4 observed-retrofit 50 mm row (U 0.68) while the SAME description lodged with "ND" fell through to the Table 18 age-band default (0.40 for bands A-G). The ND-vs-NI sentinel is lodging-software noise; "(assumed)" means the insulation PRESENCE is an age-band assumption, not an observed retrofit, so Table 18's "thickness cannot be determined" clause governs both forms. The bare "insulated" description (observed retrofit, no qualifier) keeps the 50 mm row. Evidence: the 27-cert loc4+NI corpus cohort was systematically under-rated (~-1 SAP; zero positive movers among "(assumed)" certs), vs the 102-cert loc4+ND cohort already on Table 18 at ~0 bias. Gauge: within-0.5 74.2% -> 75.5%, SAP MAE 0.721 -> 0.708, CO2 MAE 0.09 -> 0.074, PE MAE 3.5 -> 3.2 (floors ratcheted). Unit-pinned in test_rdsap_uvalues (assumed -> Table 18; bare-insulated -> 0.68 regression guard); integration pin in test_heat_transmission updated; RealCertExpectation pinned for 10094975827 (66 = lodged, was -1.87). Ledger: C002 103001004 deferred pending Elmhurst arbitration (single-immersion off-peak pricing + declared cylinder loss vs Table-2); C006 quadruplet marked ⚠ (LIG-21.0 software honours lodged 0 extract fans literally vs the Elmhurst-validated 0=unknown->default convention, worksheet case 46). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| 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 | ||
| modelling_audit.md | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| 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