ff25746f44
Mirror of the cohort-2 Summary-path sweep that closed across
S0380.30..38: for each of the 38 cohort-2 certs whose API JSON was
fetched in S0380.39, drive the full API chain (`from_api_response`
→ `cert_to_inputs` → `calculate_sap_from_inputs`) and assert
`sap_score_continuous` vs the worksheet's lodged SAP at abs <= 1e-4.
Per cross-mapper parity ([[feedback-cross-mapper-parity-via-cascade]]):
the SAP cascade is the load-bearing equivalence check between
EpcPropertyData produced by from_api_response and from_elmhurst_site_notes.
If both paths hit the worksheet at 1e-4, they're cascade-output-
equivalent for load-bearing fields — strictly stronger than a noisy
structural EpcPropertyData diff.
Two parametrized tests, both green at HEAD:
- test_api_cohort_2_full_chain_sap_matches_worksheet_at_1e_minus_4:
34 certs that hit the worksheet at 1e-4 on the API path immediately
(the cascade can't tell which mapper produced the EPC).
- test_api_cohort_2_open_cert_residual_matches_current_pin:
4 certs that don't yet hit 1e-4 — pinned at their current cascade
output as forcing functions per [[project-api-to-sap-residual-test]].
When a follow-up slice closes the underlying mapper/spec gap, the
cascade output moves and the pin fires, forcing the cert to migrate
from _COHORT_2_API_OPEN to _COHORT_2_API_CLOSED.
Open cohort residuals (handover to Slice C+):
- 0300/1536/9380: tight +0.42..+0.44 band — likely a single shared
cascade-spec gap (API-mapper-specific, since Summary path hits 1e-4)
- 2102: -6.30 — Summary test (test_summary_2102_secondary_heating_
routes_house_coal_for_open_fire) shows the cert lodges house-coal
open-fire secondary heating; API mapper likely routes secondary
fuel differently. Probe `secondary_heating` block first.
Test suite: 712 → 750 pass (0 fails). Pyright net-zero on touched file.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| 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