efdb8e816c
ADR-0054 gives a prediction conditioned by an expired Historic EPC the source
"expired", so reporting can tell "no EPC at all" apart from "only an expired
one". It was implemented in IngestionOrchestrator — but the pipeline that
actually runs is modelling_e2e, which predicts through its own _predict_epc and
never adopted it. Result: zero `expired` rows have ever been written, and
`epc_property.source` holds only 'lodged' and 'predicted'.
Two things kept the flavour unreachable here, and both had to go:
- _predict_epc never looked at the historic backup at all, so no prediction
was ever conditioned.
- _flush_writes passed the literal source="predicted", and _PropertyWrite had
no field to carry a flavour, so one would have been dropped before the write
even if computed.
_predict_epc now mirrors IngestionOrchestrator._predict: the expired cert's
stable attributes fill the gaps Landlord Overrides left (overrides still win
where both speak) and condition the cohort, and it returns the source alongside
the EPC. _PropertyWrite carries it; _flush_writes persists it. save_batch
already groups deletes by source family, so a mixed predicted/expired batch
clears the shared slot correctly.
Ships dark: the reader is built only when HISTORIC_EPC_S3_ROOT is set, so
without it every prediction stays plain "predicted", exactly as today.
DEFAULT_S3_ROOT names the dev bucket, so defaulting to it would have a prod
lambda silently reading dev data — Terraform sets the var per environment.
Note this also rescues properties that currently fail to model outright: an
expired cert can supply the property_type no override resolved, where today
that raises UnresolvedPropertyTypeError.
Co-Authored-By: Claude Opus 4.8 (1M context) <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 | ||
| modelling_audit.md | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| 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