Welcome to the hfs repository!👋 This library provides several hierarchical feature selection algorithms.

Many real-world settings contain hierarchical relations. While in text mining, words can be ordered in generalization-specialization relationships in bioinformatics the function of genes is often described as a hierarchy. We can make use of these relationships between datasets' features by using special hierarchical feature selection algorithms that reduce redundancy in the data. This can not only make tasks like classification faster but also improve the results. Depending on use case and preference you can choose from lazy and eager hierarchical feature selection algorithms in this library.

The package cannot be installed with pip or conda yet so to create your package, you need to clone the hfs repository:

$ git clone https://github.com/sm1lla/hfs.git

We recommend that you create a new virtual environment for hfs in which you install the required packages with:

$ pip install -r requirements.txt

Here is a simple example of how to use one of the hierarchical feature selection algorithms implemented in hfs:

from hfs import SHSELSelector

# Initialize selector
selector = SHSELSelector(hierarchy)

# Fit selector and transform data
selector.fit(X, y, columns=columns)
X_transformed = selector.transform(X)

For detailed information on how to use hfs, check out our complete documentation at https://hfs.readthedocs.io. 📖

There you can find not only the API documentation but also more examples, background information on the algorithms we implemented and results for some experiments we performed with them.

We welcome contributions! If you would like to contribute to the project, feel free to create a pull request.

Happy feature selecting!