2828bf988d
Adds `test_summary_0380_full_chain_sap_matches_worksheet_pdf_exactly` plus the `_SUMMARY_000899_PDF` fixture constant. The test pins the Summary → ElmhurstSiteNotesExtractor → EpcPropertyDataMapper → cert_to_inputs → calculator chain for cert 0380-2471-3250-2596-8761 (Mitsubishi PUZ-WM50VHA ASHP, PCDB index 104568, semi-detached bungalow age D, TFA 60.43 m²) against the unrounded SAP lodged on the `dr87-0001-000899.pdf` worksheet "SAP value" line: **88.5104**. Opens the Summary-path workstream for the 7-cert ASHP cohort. API path is already at the spec-precision floor (Δ +0.0594, pinned by slice 102f). The Summary path becomes the canonical reference once it closes to 1e-4 — the boiler precedents (cert 001479 worksheet 69.0094, cert 0330 worksheet 61.5993) followed the same Summary- first ordering. Diagnostic baseline (printed by the probe in the handover): Summary mapper main_heating_category: None (expected: 4 / HP) Summary mapper main_heating_index_number: 104568 (expected: 104568) Summary path SAP: 33.7920 Δ vs 88.5104: -54.7184 Failure mode is exactly what the handover predicts: the Elmhurst extractor surfaces the PCDB index correctly but leaves `main_heating_category=None`, so `cert_to_inputs` misroutes off the Appendix N3.6/N3.7 heat-pump path and lands on a default boiler-ish cascade. First slice to fix in slice 2: surface `main_heating_category=4` from the Elmhurst Summary heating block when the PCDB index resolves to a HP record. Pyright: 0 errors on the test file. Convention: 1e-4 tolerance per `feedback_zero_error_strict` and the closed-boiler precedent (no widening until cascade matches at 1e-3 and the residual is documented). AAA literal headers per `feedback_aaa_test_convention`. `abs(diff)` not `pytest.approx` per `feedback_abs_diff_over_pytest_approx`. Baseline shifts from "669 pass + 10 pre-existing fail" to "669 pass + 11 fail" — the new fail is the forcing function for the workstream. Refs: - backend/documents_parser/tests/test_summary_pdf_mapper_chain.py:494 - domain/sap10_calculator/docs/HANDOVER_CERT_0380_SUMMARY_PATH.md Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
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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 | ||
| 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