YAIB is in active development. The following sections could be relevant for adding new code to our repository

The following libraries are important to the operation of YAIB:

• Pandas: Popular data structure framework.
• ReciPys: A modular preprocessing package for Pandas dataframes.
• Pytorch: An open source machine learning framework for deep learning applications.
• Pytorch Lightning: A lightweight Pytorch wrapper for AI research.
• Pytorch Ignite: Library for training and evaluating neural networks in Pytorch.
• Cuda Toolkit: GPU acceleration used for deep learning models.
• Scikit-learn: Machine learning library.
• Scikit-optimize: Used for Bayesian optimization.
• LightGBM: Gradient boosting framework.
• GIN: Provides a lightweight configuration framework for Python.
• Wandb: A tool for visualizing and tracking machine learning experiments.
• Pytest: A testing framework for Python.

• HyperImpute: Imputation library for MissForest and GAIN.
• PyPOTS: Imputation library.

python -m pytest ./tests/recipes
coverage run -m pytest ./tests/recipes
# then use either of the following
coverage report
coverage html

For development purposes, we use the Black package to autoformat our code and a Flake8 Linting/CI check:

black . -l 127
flake8 . --count --max-complexity=14 --max-line-length=127 --statistics