Usage

pybear is a set of data analytics tools that use the scikit-learn API. It is designed to enhance your data analytics experience when used in conjunction with other packages that follow that same API. Some packages that follow that API include:

xgboost

lightgbm

imbalanced-learn

pybear drops right into workflows that use those packages. For example, all pybear classes can be incorporated into a scikit pipeline.

Check out the installation section for information on how to install pybear.

The folder structure of pybear is nearly identical to scikit-learn. This is so those that are familiar with the scikit layout and have experience with writing the associated import statements have an easy transition to pybear. The pybear subfolders are base, feature_extraction, model_selection, new_numpy, preprocessing, and utilities.

You can import pybear’s packages in the same way you would with scikit. Here are a few examples of how you could import and use pybear modules:

from pybear.preprocessing import InterceptManager as IM

trfm = IM()
trfm.fit(X, y)

from pybear import preprocessing as pp

trfm = pp.ColumnDeduplicator()
trfm.fit(X, y)

See the API section of the docs for a complete listing of the available modules.