ee98dbe0ec
User-driven pivot from cascade chain-pin chase to the rigorous cohort
pattern: a hand-built EpcPropertyData that cascades to the worksheet
at 1e-4 is the ground truth for cross-mapper parity testing. Both the
Elmhurst mapper and the API mapper should ultimately produce a hand-
built-equivalent EpcPropertyData for cert 001479; every divergence
from the hand-built is a mapper bug.
This skeleton encodes the cert 001479 worksheet inputs:
- 3 building parts (Main C, Ext1 L, Ext2 C) with per-bp wall U
- Main party wall CU (cavity unfilled, U=0.50, lodged via WC_CAVITY=4)
- Cantilevered upper-storey Ext2 with `is_exposed_floor=True` (U=1.20)
- Ext2 PS sloping-ceiling roof at `roof_insulation_thickness=0`
(Slice 57 PS+pre-1950 path → Table 16 row 0 U=2.30)
- Main 300 mm joist roof insulation → U=0.14
- 8 Main windows (U=2.8, g=0.76) + 1 Ext1 window (U=1.4, g=0.72)
- Worcester Greenstar 30i (PCDF 17507) main + SAP 605 gas fire secondary
(Slice 58 mains-gas secondary fuel cost routing)
- Sheltered sides 1, 2 intermittent fans, 90% draught-proof, 23 LEDs
Adds an `001479` entry to `_FIXTURE_PINS` + `_FIXTURE_MODULES` in
`test_e2e_elmhurst_sap_score.py` with the worksheet PDF's 11
cascade-output line refs:
sap_score 69 (258)
sap_score_continuous 69.0094 "SAP value"
ecf 2.2215 (257)
total_fuel_cost_gbp 600.4001 (255)
co2_kg_per_yr 2687.3610 (272)
space_heating_kwh_per_yr 8103.7054 Σ (98c)
main_heating_fuel_kwh_per_yr 8194.7583 (211)
secondary_heating_fuel_kwh_per_yr 2025.9264 (215)
hot_water_kwh_per_yr 2358.3123 (219)
pumps_fans_kwh_per_yr 160.0000 (231)
lighting_kwh_per_yr 163.3584 (232)
Current state of the hand-built cascade vs worksheet:
Pin Cascade Expected PASS?
sap_score_continuous 65.99 69.01 no, -3.02
total_fuel_cost_gbp 658.92 600.40 no, +58.52
main_heating_fuel_kwh_per_yr 9359.6 8194.8 no
pumps_fans_kwh_per_yr 160.0 160.0 PASS
lighting_kwh_per_yr 163.4 163.4 PASS (after
LED/CFL split)
(... 9 others all failing by various deltas)
2/11 pins green. The remaining ~3 SAP gap means the hand-built has
input gaps that produce more loss/cost than Elmhurst's calc. Likely
suspects (slice candidates):
- HW demand: cascade likely over-counts (combi vs cylinder routing,
Tcold model)
- Internal gains: appliance + cooking energy share
- §2 ventilation tuning (chimney/flue counts, suspended-floor flag)
- Thermal mass parameter (250 default — confirm worksheet matches)
- Multiple-glazed proportion (cascade reads None → may default
unfavourably for solar gains)
Documents source-data caveat in the fixture docstring: Summary §3
says Ext1 age "M 2023 onwards"; worksheet header says "Ext1: L".
Hand-built uses 'L' to mirror the worksheet (which is the calc's
input source of truth); Elmhurst mapper produces 'M' from the
Summary — cross-mapper diff will flag this as a known caveat.
All 6 cohort cascade pins remain green at 1e-4 (66/66 fixture pins).
Pyright net-zero on the new fixture file.
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