https://github.com/jespb/python-rbdgp
https://github.com/jespb/python-rbdgp
Last synced: 10 months ago
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- Host: GitHub
- URL: https://github.com/jespb/python-rbdgp
- Owner: jespb
- Created: 2023-09-28T15:02:48.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-28T15:10:02.000Z (over 2 years ago)
- Last Synced: 2023-09-28T17:31:34.767Z (over 2 years ago)
- Language: Python
- Size: 20.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.txt
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README
This is a, easy-to-use, scikit-learn inspired version of the Random Binary Decomposition Genetic Programming (rbdGP) algorithm.
By using this file, you are agreeing to this product's EULA
This product can be obtained in https://github.com/jespb/Python-rbdGP
Copyright ©2023-2023 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 rbdGP can be used in a stand-alone fashion using the following command and flags:
$ python Main_RbdGP_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 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.
[-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.
How to import this implementation to your project:
- Download this repository;
- Copy the "rbdgp/" directory to your project directory;
- import the RbdGP class using "from rbdgp.RbdGP import RbdGP".
How to use this implementation:
$ from rbdgp.RbdGP import RbdGP
$ model = RbdGP()
$ model.fit( training_x, training_y)
Arguments for RbdGP():
operators -> Operators used by the individual and the number of arguments (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)
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 = RbdGP() -> starts the model;
$ model.fit(X, Y) -> fits the model to the dataset;
$ model.predict(dataset) -> Returns a list with the prediction of the given dataset.
How to edit this implementation:
Fitness Function ( rbdgp.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;
- You may use the getTrainingPredictions() and getTrainingSet() to obtain the models prediction and the training set;
- You can also explore the behind the standard fitness function;
- Warning: RbdGP evaluates every model in every run, as such, I do not recomend complex fitness functions. You should invest in fast evaluation methods to train a population.
Classification method ( rbdgp.Individual ):
- Change the trainModel() method to use your own classifier;
- Assuming it is a scykit-learn implementation, you may only need to change the first few lines of this method;
- Warning: RbdGP evaluates every model in every run, as such, I do not recomend complex classification model. You should invest in fast classification methods to train a population and the use a more complex method (if you wish) on the final model.
Reference:
Liao, L., Pindur, A.K., Iba, H.: Genetic Programming with Random Binary Decomposition for Multi-Class Classification Problems. IEEE-CEC (2021)
DOI: 10.1109/CEC45853.2021.9504967