Biobjective gradient descent for Feature Selection (BFS)
Code for BFS, a biobjective gradient descent feature selection method based on network sparsification.
Installation
To install BFS from git, clone the project using the following command:
git clone https://forge.ibisc.univ-evry.fr/tissa/BFS/tree/master/BFS.git
To reproduce the conda environment with the required packages and appropriate versions, use the following command:
For Mac users:
conda env create -f BFS_mac.yml
For Linux users:
conda env create -f BFS_linux.yml
To install BFS using pip:
python3 -m pip install BGFS
Usage
If downloaded using git, before using BFS, first go to the source files' directory using the following command:
cd BFS/source
From there or when downloaded with pip, BFS can be loaded in a python script as any other module using the import
command.
BFS can be used with the following commands:
from BGFS.BFS import BFS model = BFS(n_attr, pref_vector1, pref_vector2, n_hidden, n_output) model.compile(init_optimizer, train_optimizer, init_learning_rate, train_learning_rate, metrics) model.fit(X_train, y_train, validation_data=(X_val, y_val))
Documentation
BFS documentation can be found on: https://bfss.readthedocs.io/
License
BFS was created by Tina Issa. It is licensed under the terms of the MIT license.