0ffc25506e
RdSAP 10 §3.12 (PDF p.26), the flats/maisonettes floor rules: "There is no heat loss through the floor if there is another flat below. Otherwise the floor area of the flat ... is ... a semi-exposed floor if there are unheated premises below it (e.g. an enclosed garage) ... Semi-exposed (sheltered) floors are treated as if they were fully exposed." §5.13 (p.48) confirms the U-value identity: Table 20 applies to exposed and semi-exposed alike. API floor_heat_loss=2 -> floor_type "To unheated space" set no exposure signal, so on a mid-/top-floor flat the dwelling-level heuristic (has_exposed_floor=False, assuming a heated dwelling below) went unopposed and the ENTIRE floor was billed at 0 W/K. 11 properties across portfolios 796+824, mean +4.02 SAP, 10 of 11 over-rating — we judged dwellings more efficient than the accredited assessment and would withhold retrofit measures they need. Exemplars: 750018 space heating -53.2% -> -8.1% vs the cert's own RHI figure, SAP +8.22 -> +1.16; 749898 -43.0% -> +0.9%, SAP +6.29 -> -1.11. SCOPED TO FLATS ON PURPOSE. §3.12 is the flats/maisonettes section, and the accredited Elmhurst worksheet disagrees with Table 20 for a house: golden cert 7536-3827-0600-0600-0276 (detached) lodges floor_heat_loss=2 on its Main part and the worksheet bills it on the BS EN ISO 13370 ground-floor cascade at U 0.97, not Table 20's 1.20. An unscoped mapper-level fix broke that pin and the uprn_100050881708 accuracy pin; the scoped fix leaves both untouched. No regressions: identical failure set and pyright count vs main; the RdSAP-21.0.1 accuracy corpus gauge is unchanged at 78.8% within-0.5 / MAE 0.625 (correctly — all 7 corpus certs lodging code 2 are houses). 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