71b378b9e5
The profiler flagged `mechanical_ventilation=2` as a clean systematic
over-rate: 20 certs, signed +1.90 SAP, only 5% within 0.5 (every one
positive). Root cause: the API path (`from_api_response`) dropped the
doc-level `mechanical_ventilation` field, so `sap_ventilation.
mechanical_ventilation_kind` was always None and the §2 cascade
defaulted to NATURAL — under-stating the ventilation air-change rate
(and hence heat loss) for every mechanical system. (Only the Elmhurst/
Summary path mapped it, via `_ELMHURST_MV_TYPE_TO_KIND`.)
RdSAP-Schema-21 `mechanical_ventilation` enum (epc_codes.csv) →
MechanicalVentilationKind picking the SAP 10.2 §2 (24a..d) effective-ach
formula:
0 natural -> NATURAL (24d)
1 MV (no heat recovery) -> MV (24b)
2 mechanical extract, dc (MEV) -> EXTRACT_OR_PIV_OUTSIDE (24c)
3 mechanical extract, c (MEV) -> EXTRACT_OR_PIV_OUTSIDE (24c)
5 positive input from loft -> NATURAL (loft-sourced PIV adds no
system air change per RdSAP 10 §2.6)
6 positive input from outside -> EXTRACT_OR_PIV_OUTSIDE (24c)
Code 4 (MVHR, 24a) is DEFERRED — its formula needs the lodged
heat-recovery efficiency (PCDB Table 326) the API→cascade path doesn't
yet plumb; mapping it to MVHR with a null efficiency would mis-model it
as MV, so it stays NATURAL (3 scattered certs, accurate at the median).
Unmapped integers raise `UnmappedApiCode` (mirror of `_api_sheltered_
sides` / `_api_type_1_gable_kind`).
Eval: the extract cohort (mech_vent 2/3/6) moved +1.90 -> +0.9 median
(within-0.5 5% -> 35%); 20 improved / 3 regressed (offsetting). Headline
within-0.5 54.24% -> 55.01%, within-1.0 69.64% -> 70.08%, mean|err|
1.248 -> 1.233, 909 computed / 0 raises. The +0.9 residual on MEV is the
fan electricity (§2.6.4 SFP, PCDB Table 322) — a separate follow-up.
2 AAA tests; goldens + full calc/epc/parser regression green; pyright
net-zero.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
|
||
|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| 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 | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| 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