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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.

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Introduction

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.

Testing

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.

Setup

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.

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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.

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