https://github.com/jespb/python-gsgp

My implementarion of the Geometric Semantic Genetic Programming (GSGP) algorithm.
https://github.com/jespb/python-gsgp

binary-classification classification evolutionary-algorithms evolutionary-computation genetic-programming machine-learning regression regression-models semantics

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My implementarion of the Geometric Semantic Genetic Programming (GSGP) algorithm.

• Host: GitHub
• URL: https://github.com/jespb/python-gsgp
• Owner: jespb
• Created: 2019-11-08T13:07:02.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-10-03T09:10:39.000Z (over 2 years ago)
• Last Synced: 2023-10-03T20:34:46.379Z (over 2 years ago)
• Topics: binary-classification, classification, evolutionary-algorithms, evolutionary-computation, genetic-programming, machine-learning, regression, regression-models, semantics
• Language: Python
• Size: 48.8 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.txt

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README

          
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This product can be obtained in https://github.com/jespb/Python-GSGP

Copyright ©2019 J. E. Batista

This implementation of GSGP uses the following command and flags:

$ python Main_GSGP.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		

	[-dontshuffle]

		- By using this flag, the dataset will not be shuffled;

		- By default, the dataset is shuffled.

	[-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 1000.

	

	[-ms mutation_step]

		- This flag expects a float with the mutation step size;

		- By default, the mutation step used is 0.1

	[-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 separated by ";"

		- Allowed operators: +;-;*;/

		- By default, the used operators are the sum, subtraction, multiplication and protected division.		

	[-ps population_size]

		- This flag expects an integer with the size of the population;

		- By default, this value is set to 500.

	[-r] 

		- States the this is a regression problem. 

		- By default the GSGP tries to classify samples as 0 or 1

	[-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.

How to import this implementation to your project:

    - Download this repository;

    - Copy the "gsgp/" directory to your project directory;

    - import the GSGP class using "from gsgp.GSGP import GSGP".

How to use this implementation:

    $ from gsgp.GSGP import GSGP

    $ model = GSGP()

    $ 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_initial_depth	-> Max initial depths of the individuals (default: 6)

    population_size		-> Population size (default: 500)

    max_generation		-> Maximum number of generations (default: 1000)

    tournament_size		-> Tournament size (default: 5)

    elitism_size		-> Elitism selection size (default: 1)

    mutation_step		-> Mutation step value (default: 0.1)

    threads 			-> Number of CPU threads to be used (default: 1)

    random_state		-> Random state (default: 42)

    verbose				-> Console prints during training (default: True)

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 = GSGP()	-> 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. (use after training)

How to edit this implementation:

    Fitness Function ( gsgp.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.

Reference:

    Moraglio A., Krawiec K., Johnson C.G. (2012) Geometric Semantic Genetic Programming. In: Coello C.A.C., Cutello V., Deb K., Forrest S., Nicosia G., Pavone M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg