6bfb0614aa
Replaces the loose collection of fixture-specific SAP score tests + parametrized lighting / pumps_fans / secondary spot-checks with a single strict cascade pin: every SapResult float field vs PDF line ref at abs=1e-4, every fixture × field pair as its own parametrized case. 66 cases (11 fields × 6 fixtures); 18 pass, 48 fail. Why: the Elmhurst corpus is a deterministic test-vector set — input lodgement, intermediate values per line ref, final SAP outputs all known to 4 d.p. To replicate SAP 10.2 exactly there is no reason to accept tolerance >0 on the final outputs. The prior pattern (per- section unit tests using PDF values as INPUTS, fixture-specific SAP tests at <=0.5 continuous, fuel-cost tests at rel=0.05 / rel=0.15) let cascade biases propagate without surfacing as named failures. Pin matrix: field | 474 | 477 | 480 | 487 | 490 | 516 -----------------------------------|-----|-----|-----|-----|-----|----- sap_score (int) | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ sap_score_continuous | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ ecf | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ total_fuel_cost_gbp | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ co2_kg_per_yr | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ space_heating_kwh_per_yr | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ main_heating_fuel_kwh_per_yr | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ secondary_heating_fuel_kwh_per_yr | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ hot_water_kwh_per_yr | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ lighting_kwh_per_yr | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ pumps_fans_kwh_per_yr | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Each failing test name is the work queue. No tolerance widening, no xfail — a failing pin is a named calculator bug. Subsequent slices close them one at a time. Existing loose-tolerance tests in test_fuel_cost.py (rel=0.15 for 000474 and rel=0.05 for 000490) are subsumed by the new total_fuel_cost_gbp pin at abs=1e-4 and will be removed in 19b. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| 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_backlog.sh | ||
| 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