8bf7bd116d
A cert lodging its horizontal dimensions measured externally (measurement_type=2) must have its floor area and exposed perimeter converted to internal dimensions before any SAP use (RdSAP 10 §3.4 + Table 2, p.17-18). measurement_type was read nowhere (the `# What is this?` field), so external certs carried a TFA ~11-15% too large — inflating occupancy and understating every per-m2 output (CO2/m2, PE/m2). Plumb measurement_type onto EpcPropertyData, and add a post-mapping _with_internal_dimensions step: compute ONE whole-dwelling area/perimeter ratio from the ground-floor footprint via the Table 2 built-form equation (§3.4 Note 4 — not a per-storey conversion, which over-corrects multi-part mid-terraces), apply it to every storey's area/perimeter, reduce party walls by 2w (Note 5), and refresh total_floor_area_m2 (read for occupancy N). Wall thickness w is the lodged main-dwelling value, else the Table 3 default (p.19). Room-in-roof floor area is always internal (§3.1) and left untouched. Scoped to the RdSAP-Schema reduced-data family (17.0-21.x) where per-storey external dims are the lodgement standard; legacy SAP-Schema-15.0/16.x are plumbed but not converted. Corpus: within-0.5 81.0% -> 81.2%, MAE 0.571 -> 0.570, PE 2.5 -> 2.4. Cert 100091435353 TFA 128.7 -> 111.6 (lodged 112), SAP 66.23 -> 65.33 (+1.2 -> +0.3); mean |dSAP vs lodged| over the 12 external certs 0.556 -> 0.429. Closes #1669. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| .claude/skills | ||
| .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 | ||
| 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