9a509e4102
The cert encodes filled-cavity walls as (wall_construction=4 cavity, wall_insulation_type=2 filled, wall_insulation_thickness="NI"). The previous cascade parsed "NI"→0 and ran the thickness-bucketed table, returning U=1.5 (the "Cavity as built" row) — treating retrofit-filled cavities as if they were uninsulated. Spec (RdSAP 10 Table 6, page 33) has a dedicated "Filled cavity" row at U=0.7 for bands A-E, 0.40 at F, 0.35 at G-H, and "as built" from band I onward. Adds: - WALL_INSULATION_FILLED_CAVITY constant (code 2 per RdSAP schema, confirmed empirically on 8 000 corpus certs against walls.description) - _CAVITY_FILLED_ENG row in domain.ml.rdsap_uvalues - dispatcher in u_wall when (construction=cavity, insulation_type=2) - wall_insulation_type plumbing through heat_transmission_from_cert Parity probe (300 certs, seed=7) before → after: - PE MAE 57.28 → 48.99 (-8.3) - PE bias 51.56 → 42.07 (-9.5) - Band C bias +65.3 → +47.8 (-17.5) - Band D bias +67.9 → +45.7 (-22.2) - Band E bias +77.0 → +58.8 (-18.2) - Band F bias +43.8 → +25.4 (-18.4) - Band K-L bias unchanged (filled-cavity row falls back to as-built from band I onward per spec footnote; correct no-op) Future slices already lit up by the same enumeration: - type=1 external / type=3 internal insulation rows (~440 certs) - type=6 filled + external / type=7 filled + internal (~22 certs) - type=None "Average thermal transmittance X W/m²K" string parse (1 358 certs — biggest follow-up) 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