561e1b8b49
Wire Sap10Calculator into PropertyBaselineOrchestrator as a non-load-bearing shadow runner. For each property it scores the Effective EPC beside the load-bearing Lodged/Effective write, catches any strict-raise -> log.error (never aborts the batch), and on success log.warning's divergence from Lodged: SAP |continuous - lodged| > 0.5; PEUI/CO2 > 1% relative (CO2 after kg->tonnes). Every line is tagged with sap_version so SAP-10.2 signal separates from older-spec drift (ADR-0010 Validation Cohort). Per ADR-0013, Calculated SAP10 Performance is not a persisted third value-set: effective = calculated in every baselining scenario, so the calculator IS the mechanism that produces Effective Performance (the Rebaseliner). It runs in shadow only while being hardened; when overrides/estimation land it is promoted to drive Effective and the failure posture flips to abort (ADR-0012, calculator now load-bearing). No table change. - ADR-0013 + CONTEXT (Calculated SAP10 Performance / Effective Performance / Rebaselining) record the decision. - CalculatorShadow port + LoggingCalculatorShadow + Calculator protocol. - FakeCalculatorShadow for orchestrator unit tests. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .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 | ||
| 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