🎯 A comprehensive gradient-free optimization framework written in Python

Last update: Dec 26, 2022

Overview

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.

Comments

Run flake8 in warning only mode on Python 2 and 3

This will help us find and fix the Python 3 syntax errors (print_function, etc.) A step towards the resolution of https://github.com/100/Solid/issues/6

opened by cclauss 6
Simulated annealing: bug in run method
Description of the bug

The run() method of the SimulatedAnnealing class has a bug when the annealing method does not find a better state than the initial one.

When does it happens

The bug happens when the annealing algorithm fails to find a better state than the initial one. This can happen when the maximum number of steps is low or when the initial guess is already very good.

What is the current behaviour

The tuple returned by the run() method is (None, cost_of_initial_state).

How to fix

Add the line

self.best_state = deepcopy(self.current_state)

between L142 and L143.
opened by nelimee 0
Correction of EA and GA for nondeterministic fitness functions

Correction of an issue that occurs when the fitness function is nondeterministic (shuffled cross-validation for example). In the _select_n method, the total fitness is computed according to the stored fitnesses, but the probs variable is computed according to recalculated fitness values. This slight change makes the method use the stored fitnesses at each time, which solves the problem. This also makes the method run much faster (especially when the fitness function has a high complexity) by removing unnecessary calls to _fitness.

opened by miraaitsaada 0
More Algorithms
Of course, more algorithms are always great.

Some suggestions:

Coordinate descent

Ant colony optimization

Differential evolution

Cuckoo search

Cross-entropy method

enhancement help wanted
opened by 100 0
Numerical Stabilitity

It would be good to find all of the instances where the algorithms may be unstable and handle these cases appropriately (such as overflow). Some cases are handled, but there are probably more.
bug help wanted

opened by 100 0
Better Testing?

Currently, the testing just makes sure that the algorithm runs without error on a toy problem.

It would be nice to do something more akin to unit testing, but I'm not quite sure how to do it in this situation since a lot of the testable functionality is provided by the user.
enhancement help wanted question

opened by 100 0

Releases(0.11)

0.11(Jun 19, 2017)

Source code(tar.gz)
Source code(zip)
0.1(Jun 19, 2017)

Source code(tar.gz)
Source code(zip)

Owner

Devin Soni

GitHub Repository https://100.github.io/Solid/

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