195336b7e1
Removes:
- environmental_impact_current (SAP-derived rating, leaks into co2 target)
- energy_rating_average (average of sap_score + potential, direct leak)
Adds:
Doors draughtproofed_door_count, insulated_door_u_value
Hot water cylinder_insulation_type, cylinder_thermostat,
secondary_heating_type
Ventilation mechanical_vent_duct_placement, _duct_insulation,
_duct_insulation_level, _measured_installation
Lighting low_energy_fixed_lighting_bulbs_count,
fixed_lighting_outlets_count,
low_energy_fixed_lighting_outlets_count
Windows window_avg_glazing_gap_mm, window_avg_frame_factor,
window_pct_permanent_shutters_insulated
Main dwelling room_in_roof_floor_area_m2, alternative_wall_count,
alternative_wall_area_m2, flat_roof_insulation_thickness_mm,
wall_thickness_measured
Element counts wall_count, roof_count, floor_count,
main_heating_count_elements, main_heating_controls_present
Wind wind_turbine_hub_height_m, wind_turbine_rotor_diameter_m
Flat flat_unheated_corridor_length_m
Addendum addendum_stone_walls, addendum_system_build,
addendum_numbers_count
LZC lzc_energy_sources_count
Secondary part secondary_dwelling_present + 11 fabric features
(wall/roof/floor construction + insulation + thickness
+ area + heat-loss perimeter) + other_building_parts_count
Wires through schema -> domain -> mapper: adds Addendum dataclass,
lzc_energy_sources, mechanical_vent_duct_insulation_level. Also fixes
_measurement_value to accept raw dicts (from_dict left some Measurement
fields as dict when they weren't typed as a dataclass).
Results at N=25,000 2026 RdSAP certs:
sap_score MAPE=0.043 sMAPE=0.036 R^2=0.891
co2_emissions sMAPE=0.106 R^2=0.929
peui_raw MAPE=0.087 sMAPE=0.084 R^2=0.860
peui_ucl MAPE=0.079 sMAPE=0.076 R^2=0.866
space_heating_kwh MAPE=0.112 sMAPE=0.108 R^2=0.947
hot_water_kwh MAPE=0.071 sMAPE=0.069 R^2=0.854 (+0.082 R^2 vs 15b)
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/adr | ||
| 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