Welcome to SAGA_optimize’s documentation!

SAGA_optimize.py

This module provides the SAGA class to find the optimal solutions to a set of parameters based on a given energy function with the simulated annealing and genetic algorithm, and the ElementDescription class describing the parameter. The Guess class stores a set of ElementDescription instances to a given energy function and the Population class collects a group of Guess instances.

class SAGA_optimize.ElementDescription(low=0, high=0, name='', value=None, mutate=None)[source]

ElementDescription class describes the optimized parameter to a given energy function.

__init__(low=0, high=0, name='', value=None, mutate=None)[source]

ElementDescription initializer.

Parameters:
  • low (double) – minimum value for this element; OPTIONAL if immutable value specified.
  • high (double) – maximum value for this element; OPTIONAL if immutable value specified.
  • name (str) – OPTIONAL - the name of the element.
  • value (double) – OPTIONAL - immutable value for this element.
  • mutate (str) – the method that mutates the element; DEFAULT - mutatePopulationRangedFloat.
class SAGA_optimize.Guess(elementDescriptions, elements, energy=0)[source]

Guess class collects all the optimized parameters (ElementDescription instances) to a given energy function.

__init__(elementDescriptions, elements, energy=0)[source]

Guess initializer.

Parameters:
  • elementDescriptions (list) – a list of ElementDescription instances.
  • elements (list) – a list of values for the corresponding ElementDescription instances.
  • energy (double) – the energy of the Guess to the given function.
clone()[source]

Clones everything but the energy.

Returns:the Guess instance.
Return type:Guess
class SAGA_optimize.Population(size, elementDescriptions, energyCalculation, direction=-1, initialPopulation=None)[source]

Population class collects a set of Guess instances.

__init__(size, elementDescriptions, energyCalculation, direction=-1, initialPopulation=None)[source]
Parameters:
  • size (int) – the number of Guess instances in the population.
  • elementDescriptions (list) – a list of ElementDescription instances in the Guess.
  • energyCalculation – the given energy function.
  • direction (int) – (1 or -1) for determining lowest energy.
  • initialPopulation – an initial Population instance.
class SAGA_optimize.SAGA(stepNumber, startTemperature, temperatureStepSize, alpha, populationSize, energyCalculation, direction=-1, elementDescriptions=None, startPopulation=None, initialPopulation=None, crossoverRate=0.1, crossover=None, acceptedCriteria=None, mutationRate=1, annealMutationRate=1, maxEnergy=None, crossoverProbabilities=None, validGuess=None, bestOperation=None, bestResultsFile=None, allResultsFile=None)[source]

Implements a stimulated annealing / genetic algorithm optimization strategy.

__init__(stepNumber, startTemperature, temperatureStepSize, alpha, populationSize, energyCalculation, direction=-1, elementDescriptions=None, startPopulation=None, initialPopulation=None, crossoverRate=0.1, crossover=None, acceptedCriteria=None, mutationRate=1, annealMutationRate=1, maxEnergy=None, crossoverProbabilities=None, validGuess=None, bestOperation=None, bestResultsFile=None, allResultsFile=None)[source]
Parameters:
  • stepNumber (int) – number of simple steps to perform.
  • startTemperature (double) – starting temperature.
  • temperatureStepSize (int) – number of simple steps in a temperature step.
  • alpha (double) – power of annealing rate; 1 is linear.
  • populationSize (int) – size of the population of Guesses.
  • energyCalculation – function to calculate the energy.
  • direction (int) – optimization direction; 1 is maximizing; -1 is minimizing; DEFAULT is -1.
  • elementDescriptions (list) – OPTIONAL - list of ElementDescription instances.
  • startPopulation (Population) – OPTIONAL - Population instance to use as the starting population.
  • initialPopulation (Population) – OPTIONAL - Population instance to initialize with.
  • crossoverRate (double) – fractional rate of crossover versus mutation; DEFAULT is 0.1.
  • mutationRate (int) – number of mutations to perform in creating a new Guess; DEFAULT is 1.
  • annealMutationRate – whether to anneal mutationRate with temperature; DEFAULT is 1.
  • maxEnergy (double) – OPTIONAL - override of maxEnergy for SA calculation.
  • validGuess – function that tests if a Guess object is valid. DEFAULT is None.
  • bestOperation – function to perform on best Guess object; DEFAULT is None.
addElementDescriptions(*elementDescriptions)[source]

Add elementDescriptions.

Parameters:elementDescriptions (ElementDescription) – the ElementDescription instance.
optimize()[source]

Performs the optimization.

Returns:Population.

User Guide

Description

The SAGA_optimize package provides a simple Python interface for solving boundary-value inverse problem based on the simulated annealing and genetic algorithm.

Installation

SAGA_optimize runs under Python 3.6+ and is available through python3-pip. Install via pip or clone the git repo and install the following dependencies and you are ready to go!

Install on Linux

Pip installation (method 1)
pip3 install SAGA-optimize
GitHub Package installation (method 2)

Make sure you have git installed:

cd ~/
git clone https://github.com/MoseleyBioinformaticsLab/SAGA_optimize.git
Dependecies

SAGA_optimize requires the following Python libraries:

  • JSONPickle for saving Python objects in a JSON serializable form and outputting to a file.

Basic usage

The SAGA_optimize package is used to find the optimal solutions to a set of parameters based on a given energy function calculated using the set of parameters.

Note

Read The SAGA_optimize Tutorial to learn more and see code examples on using the SAGA_optimize as a library.

The SAGA_optimize Tutorial

The SAGA_optimize package is a novel type of combined simulated annealing and genetic algorithm used to find the optimal solutions to a set of parameters based on a given energy function calculated using the set of parameters.

To perform SAGA_optimize, we need define an energy function first.

>>> def energyCalculation(elements):     # example of energy function.
>>>    energy = 0
>>>    for indedx in range(len(elements)):
>>>       energy += abs(index+1-elements[index])
>>>    return energy

Then we need to construct the parameters (ElementDescription instances) for optimization. ElementDescription instance initialization may require the range of the parameter, and its name. Default value and mutation function can also be provided. Please refer to the API documentation for detailed information.

We can construct ElementDescription instances first, and then pass them into SAGA.

>>> import SAGA_optimize
>>> element1 = SAGA_optimize.ElementDescription(low=0, high=10, name='element1')    # ElementDescription instance creation.
>>> element2 = SAGA_optimize.ElementDescription(low=0, high=10, name='element2')
>>> element3 = SAGA_optimize.ElementDescription(low=0, high=10, name='element3')
>>> element4 = SAGA_optimize.ElementDescription(low=0, high=10, name='element4')
>>> element5 = SAGA_optimize.ElementDescription(low=0, high=10, name='element5')
>>> elements = [element1, element2, element3, element4, element5]
>>> saga = SAGA_optimize.SAGA(stepNumber=100000, temperatureStepSize=100, startTemperature=0.5, elementDescriptions=elements,
                              alpha=1, direction=-1, energyCalculation=energyCalculation, crossoverRate=0.5, mutationRate=3,
                              annealMutationRate=1, populationSize=20)

Or we can create SAGA instance first, and then add the ElementDescirption instances.

>>> import SAGA_optimize
>>> saga = SAGA_optimize.SAGA(stepNumber=100000, temperatureStepSize=100, startTemperature=0.5, alpha=1, direction=-1,
                              energyCalculation=energyCalculation, crossoverRate=0.5, mutationRate=3, annealMutationRate=1,
                              populationSize=20)                     # SAGA instance creation.
>>> saga.addElementDescriptions(SAGA_optimize.ElementDescription(low=0, high=10, name='element1'), SAGA_optimize.ElementDescription(low=0, high=10, name='element2'),
                                SAGA_optimize.ElemenTDescription(low=0, high=10, name='element3'), SAGA_optimize.ElementDescription(low=0, high=10, name='element4'),
                                SAGA_optimize.ElementDescription(low=0, high=10, name='element5'))           # Add optimized parameters.

Next, we can conduct optimization.

>>> optimized_population = saga.optimize()              # the population returned after the opitimization.
>>> bestIndex = optimized_population.bestIndex          # To get the best index of the Population.
>>> bestGuess = optimized_population.bestGuess          # To get the best Guess instance of the Population.
>>> print(bestGuess)

Guess: Energy = 0.010800440413622603 Parameters: Element 1 = 0.9986605131302921 Element 2 = 2.0049781612156004 Element 3 = 3.0036003043186144 Element 4 = 3.999532176465393 Element 5 = 5.000414664475093

Indices and tables