https://github.com/drostlab/setga

Small library extracting a minimal subset from a given set that optimizes a given (set of) function(s).
https://github.com/drostlab/setga

Last synced: over 1 year ago
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Small library extracting a minimal subset from a given set that optimizes a given (set of) function(s).

• Host: GitHub
• URL: https://github.com/drostlab/setga
• Owner: drostlab
• License: mit
• Created: 2024-01-10T16:34:54.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-18T21:46:44.000Z (almost 2 years ago)
• Last Synced: 2024-11-02T16:42:14.683Z (over 1 year ago)
• Language: Python
• Size: 292 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

          
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    :alt: Documentation Status

Minimal Subset Optimizer

========================

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Overview

--------

SetGA 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.

Features

--------

- **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).

Installation

-------------------

.. code-block:: bash

  pip3 install setga

Arguments

_________________

``set_size`` - size of the the set to optimize\

``eval_ind`` - fitness function (can compute multiple fitnesses, value or a list of values expected as an output)\

``stats_by`` - number of the objective to base statistic on (0 if num of genes, 1 if first user defined etc..)\

**Optionally:**

``pop_size`` -  num of solution on every island\

    

``num_gen`` - maximal number of generations \

    

``num_islands`` -  number of islands\

    

``min_max`` -  defines if user defined objectives should be minimized or maximized. Tuple of -1 (minimization) and 1 (maximization). Should have the same lenght as the fitness output.\

    

``mutation`` - type of mutation\

    

``crossover`` - type of crossover\

    

``mutation_rate`` - probability of an index to be selected for mutation\

    

``crossover_rate`` - robability of an index to be selected for crossover\

    

``weights`` - weights for weighted bit-flip mutation or weighted uniform crossover\

    

``selection`` - type of selection\

    

``ref_points`` - custom reference points for NSGA3\

    

``verbose`` - if logbook should be printed\

    

``stop_after`` - number of generations without a change in the size of selected subset across all islands to stop after\

    

``create_individual_funct`` - custom function to create the solutions for first generation\

    

    

  

Usage

-----

This package is used by `gatai `_, you might want to have a look.

Getting optimized solutions and pareto front:

.. code-block:: python

    # Example Usage

    from setga import utils, select_subset

    pop,logbook,gens,logbook, best_sols = select.run_minimizer(num_of_elements_in_the_set, fitness_function, stats_by, stats_names_list, 

                      mutation_rate = 0.001, crossover_rate = 0.02, 

                      pop_size = 150, num_gen = num_generations, num_islands = 8, mutation = "bit_flip", 

                      crossover = "uniform",

                      selection = "SPEA2", frac_init_not_removed = 0.005)

Genetic operators

------------

One can choose from all mutations and crossover DEAP provides for binary EA, where a the uniform crossover and bit-flip mutation operators have been further optimized. It s also possible to provide own genetic operators by passing a function as the mutation or crossover operator. One can also provide a list of mutation of length of the number of islands, if different mutations should be used for different islands. We provide also weighted bit-flip mutation and weighted uniform crossover, where the probability of a index being mutated (crossed over) is proportional to its weight that is passed as the optional argument "weight".

Crossover operators: 

    

    ``"uniform", "onepoint","twopoint","partialy_matched","ordered","uniform_partialy_matched","weighted"``

    

Mutation operators:

    

    ``"bit-flip","inversion", "weighted"``

Selection

----------

One can choose from all multi-objective selection operators DEAP provides. For NSGA3, uniform ref. points are used by default, but other can be specified.

Selection operators: 

    

    ``"SPEA2","NSGA2","NSGA3"``

Contributing

------------

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.

License

-------

This project is licensed under the MIT License. Feel free to use and modify the code according to the terms of this license.