FIPE Paper Experiments Reproducibility

This repository contains the code that allows to reproduce the results of the paper "FIPE: Functionnaly Identical Prunning Ensemble".

Requirements

The code is written in Python 3.10. To install the required packages, run the following command:

pip install -r requirements.txt

Usage

To run the experiments, make sure you have a valid Gurobi license and the required libraries installed. Then, from the current folder, execute the following command:

python run.py </path/to/dataset> </path/to/output> --ensemble <ensemble> --n-estimators <n_estimators> --seeds <seed1> <seed2> ... <seedn> --norm <norm>

where:

• </path/to/dataset> is the path to the dataset file in CSV format.
• </path/to/output> is the path to the output folder.
• <ensemble> is the ensemble method to use. It can be one of the following: ab for AdaBoostClassifier or rf for RandomForestClassifier or gb for GradientBoostingClassifier.
• <n_estimators> is the number of estimators to use in the ensemble.
• <seed1> <seed2> ... <seedn> are the seeds to use for the random number generator.
• <norm> is the norm to use for the FIPE algorithm. It can be one of the following: 0 for L0 norm, 1 for L1 norm, 2 for L2 norm.

The output folder will contain the experiment results in CSV format inside a subfolder named csvs. The results can be merged into a single CSV file by running the following command:

python merge.py </path/to/csv1> </path/to/csv2> ... </path/to/csvn> </path/to/output>

where </path/to/csv1> </path/to/csv2> ... </path/to/csvn> are the paths to the CSV files to merge and </path/to/output> is the path to the output folder.

Example

python run.py --all datasets/ outputs/ --ensemble ab --n-estimators 50 100 --seeds 42 47 52 --norm 1

To merge the results of the experiments, use the following command:

python merge.py outputs/csvs/* outputs/results.csv