Simple ML
Implimentations of Machine and Statistical Learning algorithms in python with added Utility, served with a Sklearn style interface. I've started this libary for practice of implimenting algorithms, but will be adding extra functionality sci-kit learn doesn't have out of box, like explainability functions, parallel training and GPU enablement
Initially appeared on gist
Getting Started
These instructions will give you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on deploying the project on a live system.
Prerequisites
Requirements for the software and other tools to build, test and push can be found in requirements.txt
Installing
TODDO- Write docker container deployment using requirements.txt
TODO- Deploy to pypi so that Library can be downloaded via
python -m pip install Simple_ML-
Running the tests
TODO- Write the automated tests for check cases using numpy arrays other than examples TODO- Impliment pandas dataframes to fit and predict models
Deployment
To use as part of larger codebase simply import Models from Models, for example-
from Models.supervised_models import Gaussian_Naive_Bayes
Contributing
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
TODO- Impliment contributing.md
Authors
- **Charlie Masters ** - Currently Sole contributer - LinkedIn
License
This project is licensed under the CC0 1.0 Universal Creative Commons License - see the LICENSE.md file for details
Acknowledgments
Please feel free to fork and contribute to appear down Here!