This repository is an extension of the EEL, in which performing a stacking with a logistic regression as a meta-classifier and as base-classifier EEL.
The experiments were conducted on a 10-fold cross-validation. We use a seed (defined in file params.json) for partitioning the datasets into folds. By using random_state=0, you will guarantee that the folds used by your algorithm are the same as the ones used by EEL.
We do not, however, set a seed for our stochastic algorithm to run, so expect slightly different results from EEL as the ones reported in the paper.
We provide a tutorial on how to run experiments based on the Anaconda distribution of Python, with the Linux OS. Once installed, create a virtual environment for the experiments:
conda create --name env_eel python=3.6 --yes
Activate the environment using
source activate env_eel
Install requirements from the file with
pip install -r requirements.txt
Finally, create a folder for meta data using
mkdir metadata
You may have to create a specific folder for each tested algorithm.
For testing EEL, simply run a command like in the following example:
python test_eel.py -d "/home/user/datasets" -m "/home/user/metadata" -p "/home/user/params.json" --n_run 10
with required parameters.
Finally, The folder visual has several graphical ammenities used for generating figures in the paper.