06a66b3dd9
Heating sub-fields can't be field-moded without breaking system coherence, so the whole SapHeating cluster is now copied as a unit from a single coherent donor rather than inherited from the structural template: the neighbour matching the cohort's modal heating signature (main fuel + category + cylinder presence), most recent among the matches (recent cert = current system). Including cylinder presence in the signature is load-bearing — it protects has_hot_water_cylinder + cylinder_insulation (a bare fuel+cat signature regressed them). Corpus (150pc/514): heating_main_control 66.3 -> 73.9% (+7.6, the target), main_fuel 92.8 -> 96.9, category 90.7 -> 95.7, water_fuel 92.8 -> 96.3, water_code 88.5 -> 95.3, has_cylinder 81.1 -> 89.7, secondary 36.2 -> 42.0. SAP MAE vs lodged 7.08 -> 6.00 (calculator floor 1.57). cylinder_insulation -13.6 corpus (tiny-n) but +33pp on the fixture; AC requires control up + fuel/category hold + SAP not worsened, all met. Gate (36-target fixture): zero regression; ratcheted main_category 0.8889->0.9444, main_control 0.7500->0.8056, water_fuel 0.9167->0.9722, water_code 0.8889->0.9444, cylinder_insulation_type 0.1667->0.5000. This is the per-component heating method ([[feedback_per_component_best_method]]): coherent donor, never field-mode. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .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 | ||
| next_claude_prompt.txt | ||
| 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