🎯 A comprehensive gradient-free optimization framework written in Python

Overview

Build Status MIT License

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

    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

    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 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

    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

    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?

    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)
A package for music online and offline rhythmic information analysis including music Beat, downbeat, tempo and meter tracking.

BeatNet A package for music online and offline rhythmic information analysis including music Beat, downbeat, tempo and meter tracking. This repository

Mojtaba Heydari 157 Dec 27, 2022
Implementation of the federated dual coordinate descent (FedDCD) method.

FedDCD.jl Implementation of the federated dual coordinate descent (FedDCD) method. Installation To install, just call Pkg.add("https://github.com/Zhen

Zhenan Fan 6 Sep 21, 2022
Wandb-predictions - WANDB Predictions With Python

WANDB API CI/CD Below we capture the CI/CD scenarios that we would expect with o

Anish Shah 6 Oct 07, 2022
[CVPR 2021] Region-aware Adaptive Instance Normalization for Image Harmonization

RainNet — Official Pytorch Implementation Region-aware Adaptive Instance Normalization for Image Harmonization Jun Ling, Han Xue, Li Song*, Rong Xie,

130 Dec 11, 2022
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Adversarial Video Generation This project implements a generative adversarial network to predict future frames of video, as detailed in "Deep Multi-Sc

Matt Cooper 704 Nov 26, 2022
A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

Use this instead: https://github.com/facebookresearch/maskrcnn-benchmark A Pytorch Implementation of Detectron Example output of e2e_mask_rcnn-R-101-F

Roy 2.8k Dec 29, 2022
Artificial Neural network regression model to predict the energy output in a combined cycle power plant.

Energy_Output_Predictor Artificial Neural network regression model to predict the energy output in a combined cycle power plant. Abstract Energy outpu

1 Feb 11, 2022
efficient neural audio synthesis in the waveform domain

neural waveshaping synthesis real-time neural audio synthesis in the waveform domain paper • website • colab • audio by Ben Hayes, Charalampos Saitis,

Ben Hayes 169 Dec 23, 2022
A python script to lookup Passport Index Dataset

visa-cli A python script to lookup Passport Index Dataset Installation pip install visa-cli Usage usage: visa-cli [-h] [-d DESTINATION_COUNTRY] [-f]

rand-net 16 Oct 18, 2022
Signals-backend - A suite of card games written in Python

Card game A suite of card games written in the Python language. Features coming

1 Feb 15, 2022
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations"

Robust Counterfactual Explanations This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations". I

Marco 5 Dec 20, 2022
PyTorch Connectomics: segmentation toolbox for EM connectomics

Introduction The field of connectomics aims to reconstruct the wiring diagram of the brain by mapping the neural connections at the level of individua

Zudi Lin 132 Dec 26, 2022
*ObjDetApp* deploys a pytorch model for object detection

*ObjDetApp* deploys a pytorch model for object detection

Will Chao 1 Dec 26, 2021
PyTorch implementation of "Optimization Planning for 3D ConvNets"

Optimization-Planning-for-3D-ConvNets Code for the ICML 2021 paper: Optimization Planning for 3D ConvNets. Authors: Zhaofan Qiu, Ting Yao, Chong-Wah N

Zhaofan Qiu 2 Jan 12, 2022
Task-based end-to-end model learning in stochastic optimization

Task-based End-to-end Model Learning in Stochastic Optimization This repository is by Priya L. Donti, Brandon Amos, and J. Zico Kolter and contains th

CMU Locus Lab 164 Dec 29, 2022
neural image generation

pixray Pixray is an image generation system. It combines previous ideas including: Perception Engines which uses image augmentation and iteratively op

dribnet 398 Dec 17, 2022
Paper Code:A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection

1. SaWDE.m is the main function 2. DataPartition.m is used to randomly partition the original data into training sets and test sets with a ratio of 7

wangxb 14 Dec 08, 2022
Face recognize system

FRS Face_recognize_system This project contains my work that target on solving some problems of FRS: Face detection: Retinaface Face anti-spoofing: Fo

Tran Anh Tuan 4 Nov 18, 2021
mmdetection version of TinyBenchmark.

introduction This project is an mmdetection version of TinyBenchmark. TODO list: add TinyPerson dataset and evaluation add crop and merge for image du

34 Aug 27, 2022