A package implementing a supervised learning model validation framework.
Project description
MVF is a pluggable ML/statistical modelling framework that allows for the easy comparison of regression models implemented in Python and R. Write simple wrapper classes for your models and compare their performance on a particular dataset. The framework executes incrementally by default, meaning you can easily add a new model, without refitting existing models.
Pluggable - Quickly add a new model. Easily switch-out a dataset.
Auditable - All results are archived. All processes are transparent.
Reproducible - All non-stochastic processes are reproducible. Stochastic processes are repeatable.
MVF seeks to bridge the gap between R&D and integration with production applications.
Getting started
For full documentation of the project and instructions on how to get started, visit the documentation site.
Main features
- Automates the supervised ML workflow with simple configuration.
- R and Python models can be plugged in easily.
For developers
Dependencies
You need Python>=3.9 and R>=4.0.
Additionally, you must have a working installation of the R6
, IRkernel
and arrow
R packages to leverage the R/Python interoperability.
Git
This project operates using two Git branches
- dev
- main
All development work should be undertaken on the development branch. The dev branch should then be merged into the master branch to deploy a new version of the package.
CI/CD
This project uses GitLab CI/CD. Current jobs:
- test_dev - Runs all tests except functional tests using pytest. Runs on commits to
dev
branch. - test_main - Runs all tests using pytest. Runs on commits to
main
branch. - build_deploy_package - Builds the Python package and deploys to PyPI. Runs on commits to
main
branch. - build_deploy_docs - Builds the documentation site and deploys to GitLab Pages. Runs on commits to
main
branch.
All CI/CD stages run in a Docker container. This project uses node:latest
for the build_deploy_docs stage and a custom R/Python container specified by the Dockerfile for the remaining stages.
Docker
To update the container in the registry, navigate to the project root and run
sudo docker login registry.gitlab.com
Enter your GitLab username and password (only for members of the project). Then run
sudo docker build -t registry.gitlab.com/tomkimcta/model-validation-framework .
sudo docker push registry.gitlab.com/tomkimcta/model-validation-framework
PyPI
The version stored in the version
file must be incremented for a deployment of the package to be successful.
Documentation
This project uses a static site generator called Docusaurus to create its documentation. The content for the documentation site is contained in documentation/docs/
. Any updates to documentation can be verified in a development server by running npm i && npm start
from the documentation/
directory.
Project details
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