7489e5ac89
Review follow-up (Khalim): the first pass made far more optional than needed — notably the whole SapBuildingPart block — and a buggy 21.0.0↔21.0.1 diff also MISSED open_chimneys_count / cfl_/led_fixed_lighting_bulbs_count / suggested_ improvements, so the original change actually mapped only 3 of the 33 skipped certs (the rest still failed on open_chimneys_count). Re-derived the exact set empirically from all 33 skipped cohort certs: widen only fields that are (a) required in 21.0.0, (b) already optional in 21.0.1, AND (c) genuinely omitted by ≥1 of those certs. Result: - KEEP optional: the 4 SapWindow refinements, the top-level vent/lighting/ door/pressure-test block (incl. the 3 previously-missed fields), 2 SapEnergySource fields, Addendum.addendum_numbers, PhotovoltaicSupply. none_or_no_details, and exactly ONE building-part field (SapBuildingPart.roof_insulation_thickness — omitted by 7 certs). - REVERT to required: the other 12 SapBuildingPart fields (construction_age_ band, wall_construction, …), MainHeatingDetail.emitter_temperature, PvBatteries.pv_battery, ShowerOutlets.shower_outlet — none of the 33 certs omit these, so they stay strict. Mapper: coalesce the count fields (wet_rooms_count, open_chimneys_count, cfl_/led_fixed_lighting_bulbs_count) to 0 like every other mapper, so the now- optional values can't reach a NOT-NULL column (also drops 4 pyright ignores). Now maps 32/33 (up from 3); the last cert hits a pre-existing pv_batteries- shape AttributeError and degrades via the ADR-0031 skip path. pyright net unchanged (43, no new errors); regression test rewritten to the real omitted set. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| .claude/skills | ||
| .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 | ||
| 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