7cfd54129b
Roofs lodged insulated at rafters carry their thickness in a DEDICATED gov-EPC API field, `rafter_insulation_thickness` (e.g. "225mm"), while `roof_insulation_thickness` stays None (rafters aren't loft joists). That field was undeclared on the 21.0.x schemas, so `from_dict` silently dropped it — the rafter certs only *looked* redacted (roof EER 2-4 = insulated, yet no thickness), and the cascade fell to the Table 18 col (2) unknown default (2.30), badly under-rating them. - declare `rafter_insulation_thickness` on RdSapSchema21_0_0/21_0_1 + EpcPropertyData.SapBuildingPart (mirrors the existing sloping_ceiling_/flat_roof_insulation_thickness dropped-field handling). - thread it through `from_rdsap_schema_21_0_0/21_0_1` (older schemas get None via getattr). - `heat_transmission` prefers `rafter_insulation_thickness` over `roof_insulation_thickness` when the part is at-rafters, so the measured RdSAP 10 §5.11.2 Table 16 column (2) row applies (225 mm → 0.25). Completes the rafters roof fix: with the real thickness read, the rafter certs are recovered rather than over-stated — cert 3100-8675-0922-8628 (band E, rafters 225mm) +8.93 → +0.43 SAP. Corpus within-0.5 67.0% (MAE 1.025) and /tmp 71.2% (MAE 0.889) — both NET ABOVE the pre-rafters baseline (66.9% / 70.6%). Worksheet harness 47/47; regression = only the 3 pre-existing fails; pyright net-zero. 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 | ||
| 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 | ||
| next_claude_prompt.txt | ||
| 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