https://github.com/jespb/python-m6gp
M6GP, a multi-objective feature engineering algorithm that aims to produce interpretable feature engineering models
https://github.com/jespb/python-m6gp
Last synced: 4 months ago
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M6GP, a multi-objective feature engineering algorithm that aims to produce interpretable feature engineering models
- Host: GitHub
- URL: https://github.com/jespb/python-m6gp
- Owner: jespb
- Created: 2024-03-19T15:28:47.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-12-03T03:42:15.000Z (6 months ago)
- Last Synced: 2026-01-26T19:48:41.661Z (5 months ago)
- Language: Python
- Homepage:
- Size: 90.8 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.txt
- Citation: citation.bib
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README
This is an easy-to-use, scikit-learn inspired version of the M6GP algorithm.
Also available through: `pip install m6gp`
By using this file, you are agreeing to this product's EULA
This product can be obtained at https://github.com/jespb/Python-M6GP
Copyright ©2023-2025 J. E. Batista
This file contains information about the command and flags used in the stand-alone version of this implementation and an explanation of how to import, use, and edit this implementation.
This implementation of M6GP can be used in a stand-alone fashion using the following command and flags:
$ python Main_M6GP_standalone.py
[-d datasets]
- This flag expects a set of csv dataset names separated by ";" (e.g., "a.csv;b.csv")
- By default, the heart.csv dataset is used
[-dsdir dir]
- States the dataset directory.
- By default "datasets/" is used
- Use "-dsdir ./" for the root directory
[-md max_depth]
- This flag expects an integer with the maximum initial depth for the trees;
- By default, this value is set to 6.
[-mg max_generation]
- This flag expects an integer with the maximum number of generations;
- By default, this value is set to 100.
[-odir dir]
- States the output directory.
- By default, "results/" is used
- Use "-odir ./" for the root directory
[-op operators]
- This flag excepts a set of operators and their number of arguments, separated by ";"
- Allowed operators: +,2 ; -,2 ; *,2 ; /,2
- By default, the used operators are the sum, subtraction, multiplication, and protected division: "+,2;-,2;*,2;/,2"
[-ps population_size]
- This flag expects an integer with the size of the population;
- By default, this value is set to 500.
[-runs number_of_runs]
- This flag expects an integer with the number of runs to be made;
- By default, this value is set to 30
[-tf train_fraction]
- This flag expects a float [0;1] with the fraction of the dataset to be used in training;
- By default, this value is set to 0.70
[-ts tournament_size]
- This flag expects an integer with the tournament size;
- By default, this value is set to 10.
[-t number_of_threads]
- This flag expects an integer with the number of threads to use while evaluating the population;
- If the value is set to 1, the multiprocessing library will not be used
- By default, this value is set to 1.
[-di minimum_number_of_dimension]
- This flag expects an integer with the minimum number of dimensions in each individual;
- This flag affects the number of dimensions in the initial individuals;
- By default, this value is set to 1
[-dm maximum_number_of_dimension]
- This flag expects an integer with the maximum number of dimensions in each individual;
- By default, this value is set to 9999
[-rs random state]
- This flag expects an integer with the seed to be used by the M6GP algorithm;
- By default, this value is set to 42
How to import this implementation to your project:
- Download this repository;
- Copy the "m6gp/" directory to your project directory;
- import the M6GP class using "from m6gp.M6GP import M6GP".
How to use this implementation:
$ from m6gp.M6GP import M6GP
$ model = M6GP()
$ model.fit( training_x, training_y, test_x (optional), test_y (optional) )
Arguments for M6GP():
operators -> Operators used by the individual (default: [("+",2),("-",2),("*",2),("/",2)] )
max_depth -> Max initial depths of the individuals (default: 6)
population_size -> Population size (default: 500)
max_generation -> Maximum number of generations (default: 100)
tournament_size -> Tournament size (default: 5)
limit_depth -> Maximum individual depth (default: 17)
threads -> Number of CPU threads to be used (default: 1)
random_state -> Random state (default: 42)
dim_min -> Minimum number of dimensions (default: 1)
dim_max -> Maximum number of dimensions (default: 9999) #The algorithm will not reach this value
Arguments for model.fit():
Tr_X -> Training samples
Tr_Y -> Training labels
Te_X -> Test samples, used in the standalone version (default: None)
Te_Y -> Test labels, used in the standalone version (default: None)
Useful methods:
$ model = M6GP() -> starts the model;
$ model.fit(X, Y) -> fits the model to the dataset;
$ model.predict(X) -> Returns a list with the prediction of the given dataset.
How to edit this implementation:
Fitness Function ( m6gp.Individual ):
- Change the getFitness() method to use your own fitness function;
- This implementation assumes that a higher fitness is always better. To change this, edit the __gt__ method in this class;
- Warning: Since M6GP is a slow method, a fitness function that escalates well with the number of features is recommended.
Classification/regression algorithm ( m6gp.Individual ):
- Change the createModel() method to use your own classifier;
- Assuming it is a scykit-learn implementation, you may only need to change one line in this method;
- Warning: Since M6GP is a slow method, a learning algorithm that escalates well with the number of features is recommended.
Reference:
@inproceedings{m6gp,
doi = {10.1109/CEC60901.2024.10612107},
url = {https://ieeexplore.ieee.org/abstract/document/10612107},
year = {2024},
month = jun,
publisher = {{IEEE}},
author = {Joao E. Batista and Nuno M. Rodrigues and Leonardo Vanneschi},
title = {{M6GP: Multiobjective Feature Engineering}},
booktitle = {2024 {IEEE} Congress on Evolutionary Computation ({CEC})}
}
You may also be interested in other works related to measuring the complexity of feature engineering models:
- https://ieeexplore.ieee.org/abstract/document/10611989
- https://www.sciencedirect.com/science/article/pii/S2210650224002992