.. image:: https://readthedocs.org/projects/setga/badge/?version=latest :target: https://setga.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status
Minimal Subset Optimizer is a small Python library designed to extract a minimal subset from a given set, optimizing a given (set of) objective(s). Based on the DEAP library.
- Subset Extraction: Automatically identifies and extracts a minimal subset from the input set optimizing given objective(s).
- Multi-objective Optimization: Optimizes the subset based on a provided objective or a set of objectives.
- Highly Customizable: Allows for customization of the crossover, mutation and selection functions (as long as they are contained in the DEAP library.
This package is used by TraP-GA, you might want to have a look.
# Example Usage
from SetMiG import select, utils
pop,pareto_front = select.run_minimizer(num_of_elements_in_the_set,fitness_function,stats_by,stats_names_list,
eval_func_kwargs=eval_func_kwargs=,
mutation_rate = 0.001,crossover_rate = 0.02,
pop_size = 150, num_gen = num_generations, num_islands = 8, mutation = "bit_flip" ,
crossover = "uniform_partialy_matched",
selection = "SPEA2",frac_init_not_removed = 0.005)res_fit = np.array([end_evaluate_individual(x) for x in pop])
pop = np.array(pop)
par = np.array([list(x) for x in pareto_front[0]])
par_fit = np.array([end_evaluate_individual(x) for x in par])utils.plot_pareto(ress,par_fit,output_folder)utils.get_results(pop,res_fit,output_folder,name_of_elements_of_the_set)Contributions to this project are welcome. If you have any ideas for improvements, new features, or bug fixes, please submit a pull request. For major changes, please open an issue to discuss the proposed modifications.
This project is licensed under the MIT License. Feel free to use and modify the code according to the terms of this license.