11393e54dc
The FE-owned `material.type` pgEnum cannot carry `secondary_heating_removal`, so pricing it through the DB catalogue raises a DataError that poisons the session — the modelling pipeline crashed on any property with a lodged secondary heater unless the measure was excluded on the Scenario. Realise the `ProductRepository` docstring's intent (DB catalogue today, a JSON file for costs the ETL does not yet supply, behind the same port): add a `CompositeProductRepository` that resolves an override source first, then the catalogue. Checking the override first keeps that Measure Type away from the DB entirely; every other type misses the override and falls through unchanged. - off_catalogue_costs.json prices it at £270 flat per-dwelling — the legacy `Costs.heater_removal` ported to the new flat model (ADR-0028): (£25 + £200 baseline) x 1.2 VAT, for the single fixed secondary a cert lodges. Contingency (0.25) is joined from config, not the file. - Wire the composite into PostgresUnitOfWork.product and run_modelling_e2e, so the first-run pipeline and the local runner both honour the overlay. - Integration test: drop the unrealistic seeded secondary_heating_removal DB rows (the pgEnum can't hold the type) and assert it is JSON-sourced (material_id is None, cost £270) end-to-end through a real Unit of Work. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
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| sap worksheets | ||
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| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
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| __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