From the grill-with-docs pass on the depth+scale phase. Splits the overloaded "valuation" into two glossary terms — Property Valuation (current market value, a Baseline attribute, mostly missing) and Valuation Uplift (plan-conditional, percentage-primary; absolute £ only when a Property Valuation exists, 2x ROI cap on the £ form). ADR-0018 records the percentage-primary decision and why (the EPC scale corpus has no market values, so a value-primary model produces nothing), plus the deferred sourcing / per-measure / rental-yield items. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
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| survey_report | ||
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| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
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| CONTEXT.md | ||
| devcontainer.sh | ||
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| pytest.ini | ||
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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