This is a, easy-to-use, scikit-learn inspired version of the M3GP algorithm.
By using this file, you are agreeing to this product's EULA
This product can be obtained in https://github.com/jespb/Python-M3GP
Copyright ©2019-2022 J. E. Batista
This file contains information about the command and flags used in the stand-alone version of this implementation and an explanation on how to import, use and edit this implementation.
This implementation of M3GP can be used in a stand-alone fashion using the following command and flags:
$ python Main_M3GP_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
[-es elite_size]
- This flag expects an integer with the elite size;
- By default, the elite has size 1.
[-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 values 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 M3GP algorithm;
- By default, this value is set to 42
How to import this implementation to your project:
- Download this repository;
- Copy the "m3gp/" directory to your project directory;
- import the M3GP class using "from m3gp.M3GP import M3GP".
How to use this implementation:
$ from m3gp.M3GP import M3GP
$ model = M3GP()
$ model.fit( training_x, training_y, test_x (optional), test_y (optional) )
Arguments for M3GP():
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)
elitism_size -> Elitism selection size (default: 1)
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 = M3GP() -> 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 ( m3gp.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 M3GP is a slow method, a fitness function that escalates well with the number of features is recommended.
Classification method ( m3gp.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 M3GP is a slow method, a learning algorithm that escalates well with the number of features is recommended.
Citation:
If you use this implementation, please cite one of the works below, where the implementation is also used:
@Article{rs13091623,
AUTHOR = {Batista, João E. and Cabral, Ana I. R. and Vasconcelos, Maria J. P. and Vanneschi, Leonardo and Silva, Sara},
TITLE = {Improving Land Cover Classification Using Genetic Programming for Feature Construction},
JOURNAL = {Remote Sensing},
VOLUME = {13},
YEAR = {2021},
NUMBER = {9},
ARTICLE-NUMBER = {1623},
URL = {https://www.mdpi.com/2072-4292/13/9/1623},
ISSN = {2072-4292},
DOI = {10.3390/rs13091623}
}
@INPROCEEDINGS{9185630,
author={Batista, João E. and Silva, Sara},
booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)},
title={Improving the Detection of Burnt Areas in Remote Sensing using Hyper-features Evolved by M3GP},
year={2020},
pages={1-8},
doi={10.1109/CEC48606.2020.9185630}
}
Reference:
Muñoz, L., Trujillo, L., & Silva, S. (2015). M3GP – multiclass classification with GP. In Genetic Programming - 18th European Conference, EuroGP 2015, Proceedings (Vol. 9025, pp. 78-91). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9025). Springer-Verlag. https://doi.org/10.1007/978-3-319-16501-1_7