fsspark is a python module to perform feature selection and machine learning based on spark.
Pipelines written using fsspark 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).
Figure 1. Feature selection workflow example implemented in fsspark.
The package documentation describes the data structures and
features selection methods implemented in fsspark.
- pip
git clone https://github.com/enriquea/fsspark.git
cd fsspark
pip install . -r requirements.txt- conda
git clone https://github.com/enriquea/fsspark.git
cd fsspark
conda env create -f environment.yml
conda activate fsspark-venv
pip install . -r requirements.txt- Enrique Audain (https://github.com/enriquea)
- Yasset Perez-Riverol (https://github.com/ypriverol)