e91e0fa33e
Both flagged mismatches were Elmhurst input errors (same silent-stale-value
contamination class as the earlier chimneys/wall-thickness bugs), not
parser or calculator bugs:
- cylinder_size: build_100010086084.py's water_heating() selected
DropDownListCylinderSize by raw value "2", but this DOM's option values
ARE their visible litre-band text (no "2" among them) — Playwright
silently no-ops on a non-matching value, leaving a prior cert's "Medium
(131-170L)" selection in place. Fixed to match by text ("Normal"), and
added the missing mapper dict entries (Normal/Large litre-suffixed
labels) to _ELMHURST_CYLINDER_SIZE_LABEL_TO_SAP10 (a real mapper-coverage
gap — the calculator raises UnmappedElmhurstLabel rather than silently
mis-mapping).
- boiler_flue_type: the boiler-code search dialog's combined "Balanced/Open
Flue" Table 4b category doesn't drive the separate, independently
selectable RadioButtonListFlueType field, which was left at an inherited
"Balanced" from a prior cert. Fixed space_heating() to explicitly select
"Open" to match the lodged gov-API code. This field isn't consumed by
Sap10Calculator (ML/generator-only), so it had zero effect on the SAP
score.
Re-downloaded elmhurst_summary.pdf/elmhurst_worksheet.pdf after the fixes
(Recommendations page confirmed clean). Elmhurst-PDF-inputs path moved
46->47 SAP from the cylinder-volume correction; gov-API SAP (53) and
Elmhurst's own worksheet (51) are unchanged. Full accuracy suites re-run
clean (67 passed, 67 skipped, 1 xfailed, same as before).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
|
||
|---|---|---|
| .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