fslite
is a python module to perform feature selection and machine learning using pre-built FS pipelines.
Pipelines written using fslite
can be divided roughly in four major stages: 1) data pre-processing, 2) univariate
filters, 3) multivariate filters and 4) machine learning wrapped with cross-validation (Figure 1).
fslite
is based on our previous work feseR; previously implemented in R and caret package; publication can be found here.
Figure 1. Feature selection workflow example implemented in fslite.
The package documentation describes the data structures and
features selection methods implemented in fslite
.
- pip
git clone https://github.com/bigbio/fslite.git
cd fslite
pip install . -r requirements.txt
- conda
git clone https://github.com/bigbio/fslite.git
cd fslite
conda env create -f environment.yml
conda activate fslite-venv
pip install . -r requirements.txt
- Enrique Audain (https://github.com/enriquea)
- Yasset Perez-Riverol (https://github.com/ypriverol)