Contains projects associated to the development of modeling products
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epr_data_exports allowing carbon and energy otimisation by removing slack 2025-07-31 19:13:16 +01:00
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recommendations minor tweaks and bug fixes for properties that failed diagnostic tests 2026-01-06 20:58:13 +00:00
sfr/principal_pitch preparing the already installed code for Peabody 2026-01-07 20:40:37 +00:00
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.gitignore fix pipeline for July 2025 data, keep lodgement date to do analysis on new rdsap standard in ML stage 2025-08-26 10:44:31 +01:00
__init__.py added checking for directory before creation and made some minor style changes 2023-08-25 15:21:17 +01:00
BaseUtility.py fixed missing task and subtask for single remote assessments 2025-11-27 17:50:26 +00:00
Makefile fixing existing unit tests 2025-05-14 16:45:24 +01:00
package-lock.json restructuring openUrpn code 2023-07-20 11:41:43 +01:00
package.json restructuring openUrpn code 2023-07-20 11:41:43 +01:00
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serverless.yml testing moving runtime to function level 2026-01-08 13:13:41 +00:00
test.requirements.txt added testing automation with tox and added new test to handle error case when fetching address from epc registry 2025-05-14 15:34:12 +01:00
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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

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