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/dataset1> <path/to/dataset2> ... <path/to/datasetn> </path/to/output> --ensemble <ensemble> --n-estimators <n1> <n2> ... <nk> --seeds <seed1> <seed2> ... <seedn> --norm <norm>

where:

• </path/to/dataset1>, </path/to/dataset2>, ..., </path/to/datasetn> are the paths to the datasets to use.
• </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
  • rf for RandomForestClassifier
  • gb for GradientBoostingClassifier.
  • lgbm for LGBMClassifier.
  • xgb for XGBClassifier.
• <n1>, <n2>, ..., <nk> are the number of estimators to use for the ensemble method.
• <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 or 1 for L1 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 agg.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 datasets/* outputs/ --ensemble ab --n-estimators 50 100 --seeds 34 42 --norm 1

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

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

Clean

In the output folder, you will have multiple files. To clean the output folder, you can use the following command:

find outputs -mindepth 1 ! -regex '^outputs/csvs$' -delete