b7f127caec
All six are `from_site_notes`-only (gov-API RdSAP corpus unmoved at 78.9%),
graded against the DB PasHub-assessment oracle (energy_rating_current,
source='lodged'), not the surveyor-entered pre_sap.
A. Manual-Entry boiler efficiency. Extractor read PCDF-Search columns that a
manual survey lacks; mapper returned None for all boilers → the generic
0.80 gas default. Now read `Type of boiler:` / `Heating System (Boiler):`
/ `Is there an open flue?` and resolve the descriptor to its SAP 10.2
Table 4b code. Moves all 7 Wythenshawe manual boilers to the oracle
(4a Hollyhedge back boiler 63.6→53.4=53; group MAE 2.42→0.42).
B. Room-in-roof gable type None → strict-raise. A form variant omits the
gable-type line (genuine field-absence); default None → party `gable_wall`
(U=0.25), mirroring `_api_type_1_gable_kind(None)`. Present-but-unknown
labels still strict-raise. Unblocks 8 & 34 Bucklow Drive.
C. Secondary `Closed room heater` unmapped → add Table 4a code 633.
Unblocks 8 Brinkshaw Avenue.
D. Alternative-wall insulation thickness never extracted → parse
`Wall insulation thickness:` and pass it through instead of the RdSAP 10
§5.4 100 mm favourable default.
E. `Number of heated rooms?` label mismatch → the PDF lodges
`Please enter the number of HEATED rooms:`.
F. HW cylinder thermostat dropped (NEW, systematic). `_map_sap_heating`
never set `cylinder_thermostat`, so a surveyed thermostat defaulted None
and the calculator applied the SAP 10.2 Table 2b Note a) ×1.3 penalty.
Map it ("Y"/"N", gated on a lodged cylinder) as the Elmhurst path does.
Fixes the two worst DB-oracle under-raters (52 Bucklow 59.4→61.8=62;
13 Kerne Grove 65.0→67.1=67).
DB-oracle cohort (148 matched): within-0.5 83.8%→87.8%, MAE 0.427→0.303.
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 | ||
| modelling_audit.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