cb4e31a135
Three mapper extensions, validated by 000516 closing to 1e-4: 1. Roof-window separation by U-value threshold. Elmhurst Summary PDFs pool roof windows into the §11 vertical-window table with no type marker. The U-value is the only reliable signal — vertical glazing in the cohort tops out at 2.80 W/m²K, while Table 24 roof windows start at 3.0+. `_is_elmhurst_roof_window` filters U > 3.0 into `sap_roof_windows`; the rest flow through the `sap_windows` path. 2. Table-24 roof-window U-value lookup. The cohort lodges Manufacturer U=3.10 for the 000516 roof window, but the worksheet's (27a) line (U_eff=2.99) reverse-engineers to a raw U=3.40 — the RdSAP10 Table 24 "Double pre 2002" roof-window default. `_elmhurst_roof_ window_u_value` keyed on glazing-type captures the +0.3 W/m²K step; falls back to the lodged U for glazing types not yet in the table. 3. `SapWindow.window_width × window_height = lodged Area` convention. The Elmhurst Summary PDF carries lodged W (2 d.p.) × lodged H (2 d.p.) AND a precomputed Area (2 d.p., not always equal to product after rounding). The cascade reads only the W×H product across §3 / §5 / §6, so flattening to `(area, 1.0)` keeps the downstream area aligned with the worksheet's rounded value rather than reconstructing W×H with its own rounding drift (e.g. 1.22 × 1.76 = 2.1472 m² vs lodged 2.15 m²). The existing `test_first_window_*` tests pinning literal W/H were updated to pin the area product (the cascade-relevant invariant). Cohort state after this slice: 000474 0.0000 ✓ Slice 47 000477 +1.1161 Elmhurst floor_ach quirk 000480 0.0000 ✓ Slice 50 000487 +1.1844 extractor still drops most §11 windows 000490 0.0000 ✓ Slice 49 000516 0.0000 ✓ THIS SLICE 4/6 closed at 1e-4. 756 tests pass; pyright net-zero (35 baseline). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| 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