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Python application Python Package using Conda

fslite


Memory-Efficient, High-Performance Feature Selection Library for Big and Small Datasets

Description

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.

Feature Selection flowchart Figure 1. Feature selection workflow example implemented in fslite.

Documentation

The package documentation describes the data structures and features selection methods implemented in fslite.

Installation

  • 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

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