d8a3702902
Closes the `sap_windows: LEN 7 vs 5` divergence by replacing the cohort hand-built's glazing-type-collapsed 5-window encoding with 7 SapWindow entries mirroring the Summary §11 1:1 — the same row breakdown the Elmhurst mapper extracts. Per-window curtain-transform U_eff aggregates to the same total as before: Group g=0.72/U=2.0: 6.22 m² across 4 rows (was 3 rows × wider W) Group g=0.76/U=2.8: 5.50 m² across 3 rows (was 2 rows × wider W) Cascade output is unchanged — all 11 cohort 000474 SapResult pins remain GREEN at 1e-4. The per-bp window apportionment from Slice 59 (`_window_bp_index` in heat_transmission_from_cert) handles both the prior int-zero `window_location` and the new "Main"/"Nth Extension" str locations the mapper surfaces; cohort 000474 has uniform per-bp wall U so the apportionment is heat-loss-invariant either way. Surfaces a previously-hidden gap: now that the LEN matches, the diff test reveals **49 per-window sub-field divergences** between the cohort `make_window` helper (API-style int codes for `glazing_type`, `window_type`, `window_wall_type`, `glazing_gap`, `data_source`, bool `permanent_shutters_present`, None `frame_factor`) and the Elmhurst mapper (Summary-style strings for the same fields + `frame_factor=0.7`). That's the next chunk to address — most likely path: normalise the Elmhurst mapper to produce API-style int codes for the window descriptive fields, so both mappers produce the same dataclass shape. The cascade reads `window_transmission_details.u_value` / `solar_transmittance` + `window_width` × `window_height` + `orientation` + `window_location` — none of the descriptive divergences listed above affect SAP output. Diff count: 1 → 49 (surface, not regression). Cohort cascade pins green; pyright 0 errors on the fixture. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| .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