b9f1f428b9
Setup for the RdSAP-21.0.1 corpus gauge campaign (74.2% within-0.5, SAP MAE 0.721 at HEAD, 1000/1000 computed): - scripts/corpus_1000/build_worklist.py — runs all 1000 corpus certs through the gauge's own path (from_api_response -> Sap10Calculator), ranks |engine - lodged| descending, clusters by dwelling signature, and (re)writes scripts/corpus_1000/worklist.md preserving per-cert statuses and notes. 258 certs outside 0.5 in 249 clusters. - C001 (worst cert, Δ +23.9): uprn 4510053280, ground-floor flat 47 m² on ASHP PCDB 100053 (Mitsubishi Ecodan 5 kW, PSR table 0.2-2.0). Dwelling PSR 2.031 -> SAP 10.2 N2 footnotes 44/45 heat-pump extension (reciprocal interpolation toward 100% at 2x largest PSR) = 305% space efficiency, which accredited Elmhurst also applies (golden case 56, record 100061). The LODGED software instead treated the out-of-range record as invalid and billed 100% direct electric + standard schedule (reproduces lodged CO2 to 1%). Lodged-software methodology gap, worklist ⚠, engine untouched; observed engine 75 pinned in test_real_cert_sap_accuracy. Gauge floors unchanged (no engine change). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| .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