Solid – A comprehensive gradient-free optimization framework in Python

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Solid is a Python framework for gradient-free optimization.

It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients , and allows for very rapid development using them.

It’s a very versatile library that’s great for learning, modifying, and of course, using out-of-the-box.

See the detailed documentation here .

Current Features:

Usage:

  • pip install solidpy
  • Import the relevant algorithm
  • Create a class that inherits from that algorithm, and that implements the necessary abstract methods
  • Call its .run() method, which always returns the best solution and its objective function value

Example:

from random import choice, randint, random
from string import lowercase
from Solid.EvolutionaryAlgorithm import EvolutionaryAlgorithm


class Algorithm(EvolutionaryAlgorithm):
    """
    Tries to get a randomly-generated string to match string "clout"
    """
    def _initial_population(self):
        return list(''.join([choice(lowercase) for _ in range(5)]) for _ in range(50))

    def _fitness(self, member):
        return float(sum(member[i] == "clout"[i] for i in range(5)))

    def _crossover(self, parent1, parent2):
        partition = randint(0, len(self.population[0]) - 1)
        return parent1[0:partition] + parent2[partition:]

    def _mutate(self, member):
        if self.mutation_rate >= random():
            member = list(member)
            member[randint(0,4)] = choice(lowercase)
            member = ''.join(member)
        return member


def test_algorithm():
    algorithm = Algorithm(.5, .7, 500, max_fitness=None)
    best_solution, best_objective_value = algorithm.run()

Testing

To run tests, look in the tests folder.

Use pytest ; it should automatically find the test files.

Contributing

Feel free to send a pull request if you want to add any features or if you find a bug.

Check the issues tab for some potential things to do.

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