58d5b17145
Build a geographically DENSE postcode-clustered corpus to test cross-postcode geo expansion (the handover's anticipated "real geo payoff"). The gov EPC API has no area/prefix search (a partial postcode 400s; the old opendatacommunities partial-search API is decommissioned), so neighbourhood enumeration is external: seed K postcodes nationally, expand each via postcodes.io's nearest-postcode endpoint into every unit within RADIUS_M, pull each one's full EPC cohort. postcodes.io is a corpus-BUILD dependency only — the predictor stays pure. Same on-disk layout as the scattered corpus, so load_corpus + the coords resolver consume it unchanged. MEASURE-FIRST RESULT — cross-postcode expansion is a NO-GO. On a 2-seed pilot (York YO19 + Islington N51, 81 postcodes / 1558 certs, 140 SAP-10.2 targets), pooling nearby postcodes regresses accuracy across the board: same-postcode FA_MAE 9.53 wall 92% age 72% floor_con 85% cylinder 91% cross <=0.3km FA_MAE 13.1 wall 80% age 61% floor_con 82% cylinder 79% Even as a thin-cohort top-up it hurts (thin n=18: FA 5.24 -> 7.15). Root cause: the postcode boundary is itself a strong homogeneity prior (a postcode is one coherent street/development), so same-postcode neighbours beat geographically near cross-boundary ones even when the home postcode is sparse (and they rarely are — median same-postcode cohort here is 34). Geo-proximity helps WITHIN a postcode (#1227) but does not survive crossing the boundary. Cross-postcode geo closed; geo weighting stays intra-postcode. Tooling kept (reusable). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| backend | ||
| backlog | ||
| datatypes | ||
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| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
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| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
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| CONTEXT.md | ||
| devcontainer.sh | ||
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| next_claude_prompt.txt | ||
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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