41ded4740e
datatypes/epc/domain/mapper.py's from_rdsap_schema_{17_0,17_1,18_0,19_0,
20_0_0,21_0_0} all stored a lodged sap_windows[].glazing_type verbatim
instead of routing it through _api_cascade_glazing_type (which
_api_sap_window, used by 21_0_1, already does correctly). The raw API code
is the RdSAP-21 glazing enum (e.g. code 1 = "DG pre-2002"), not the SAP
10.2 Table 6b cascade enum the calculator's U/g-value tables are keyed on
(cascade code 1 = single glazed) -- so any lodged window whose raw code
happened to collide with a different cascade meaning was silently
mis-rated.
Found while validating PR #1503 (property 753950 / uprn 100021969385)
against Elmhurst: our engine's windows_w_per_k was 82.27 vs Elmhurst's
51.44 -- the dwelling's larger window (91% of glazed area) lodges raw
glazing_type=1 and was being modelled as single glazed instead of double,
the dominant contributor to a SAP 42 vs Elmhurst 44 gap. Fixing this
narrows windows_w_per_k to 50.26 (Elmhurst 51.44, ~2% off) and moves the
engine's score to 43.
Extracted the 5 duplicated reduced-schema-window blocks (17.0/17.1/18.0/
19.0/20.0.0, which lodge a minimal window shape with no glazing_gap/
frame_factor/transmission fields) into a shared _reduced_field_api_sap_
window helper that also now populates window_transmission_details/
frame_factor via the SAP10 lookup instead of leaving them None. 21.0.0's
richer inline block gets the same one-line cascade fix.
+6 regression tests (one per affected schema seam + 21.0.0). Full domain/
schema/prediction/comparable-properties/sap10_calculator/modelling_e2e
suites pass (2746 passed, only the 2 pre-existing unrelated failures);
component-accuracy gate 26/26 unaffected.
|
||
|---|---|---|
| .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