7f17de84aa
Two mapper extensions, both validated by 000490 closing to 1e-4: 1. Secondary heating extraction. Elmhurst Summary PDFs lodge the secondary heating SAP code in the §14.1 Main Heating2 sub-section (between "14.1 Main Heating2" and "14.1 Community Heating") — not in the §14.0 Main Heating1 block where the main system lives. `ElmhurstMainHeating` gains a `secondary_heating_sap_code` field; the extractor reads it from the right section; the mapper threads it through to `SapHeating.secondary_heating_type`. The cascade then applies Table 11's 10% secondary fraction. 2. Sheltered-sides derivation per RdSAP §S5. The Summary PDF doesn't lodge per-dwelling sheltered-sides; the value is derived from built-form (Detached=0, Semi-Detached=1, End-Terrace=1, Mid- Terrace=2, Enclosed Mid-Terrace=3, Enclosed End-Terrace=2). `_map_elmhurst_ventilation` now takes built_form and populates `SapVentilation.sheltered_sides`. The table is cross-checked against U985-0001-NNNNNN.pdf line (19) across the 6 worksheet fixtures. Cohort SAP deltas after this slice (target 1e-4): 000474 0.0000 ✓ Slice 47 000477 +2.6555 diagnosis pending (lighting bulb count diff) 000480 +4.1955 diagnosis pending 000487 +4.4553 extractor still drops most windows 000490 0.0000 ✓ THIS SLICE 000516 +1.5162 roof-window separation Pyright net-zero on touched files (35 errors, same baseline). 755 tests pass (up from 754 — new `test_summary_000490_full_chain_sap_ matches_worksheet_pdf_exactly`). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|---|---|---|
| .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