b77fe26892
Completes the First Run spine. Replaces the #1130 stub FirstRunPipeline with the real three-stage composition and wires it into the handler. - `FirstRunPipeline.run(command)` sequences Ingestion → Baseline → Modelling, threading **only** `property_ids` between stages (and `scenario_ids` into Modelling, off the command — never a prior stage's output). Stages are injected behind thin `IngestionStage` / `BaselineStage` / `ModellingStage` Protocols (the EpcFetcher/SolarFetcher idiom), so the handler owns wiring and tests substitute fakes (ADR-0011). - `ModellingOrchestrator` stub + `ScenarioRepository` / `MaterialsRepository` seam ports — `run(property_ids, scenario_ids)` reads through repos, does no scoring yet. Method shapes deferred to the Modelling per-service grills (Scenario / Scenario Phase / Snapshot / Optimised Package / Plans are rich — not pre-empted here). - Handler delegates to the real pipeline via `build_first_run_pipeline` (Postgres-backed repos off the session). The Ingestion source clients (EPC API / Google Solar / geospatial S3) are isolated behind one `_source_clients_from_env` seam that raises until the deploy/Terraform config settles — out of scope for this slice. Subtask complete/failed + CloudWatch URL still come from `@subtask_handler`. Integration test (the criterion's centrepiece): wires REAL Ingestion + REAL Baseline + stub Modelling through a shared fake EPC repo, with a repo-backed PropertyRepo composing the Property from that slice. Proves Baseline reads the very EPC Ingestion persisted — the through-repos hand-off, no in-memory coupling. Plus a composition test pinning stage order + only-property_ids threading. TDD, one test → one impl. pyright strict clean; AAA layout. 116 pass in the tests/ tree, no regressions. 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 | ||
| 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