catlearn.ga

catlearn.ga.algorithm

The GeneticAlgorithm class methods.

class catlearn.ga.algorithm.GeneticAlgorithm(fit_func, features, targets, population_size='auto', population=None, operators=None, fitness_parameters=1, nsplit=2, accuracy=None, nprocs=1, dmax=None)

Bases: object

Genetic algorithm for parameter optimization.

search(steps, natural_selection=True, convergence_operator=None, repeat=5, verbose=False, writefile=None)

Do the actual search.

Parameters:
  • steps (int) – Maximum number of steps to be taken.
  • natural_selection (bool) – A flag that when set True will perform natural selection.
  • convergence_operator (object) – The function to perform the convergence check. If None is passed then the no_progress function is used.
  • repeat (int) – Number of repeat generations with no progress.
  • verbose (bool) – If True, will print out the progress of the search. Default is False.
  • writefile (str) – Name of a json file to save data too.
population

The current population.

Type:list
fitness

The fitness for the current population.

Type:list

catlearn.ga.convergence

Functions to check for convergence in the GA.

class catlearn.ga.convergence.Convergence

Bases: object

Class to check convergence.

no_progress(fitness, repeat)

Convergence based on a lack of any progress in the search.

Parameters:
  • fitness (array) – A List of fitnesses from the search.
  • repeat (int) – Number of repeat generations with no progress.
Returns:

converged – True if convergence has been reached, False otherwise.
Return type:

bool
stagnation(fitness, repeat)

Convergence based on a stagnation of the population.

Parameters:
  • fitness (array) – A List of fitnesses from the search.
  • repeat (int) – Number of repeat generations with no progress.
Returns:

converged – True if convergence has been reached, False otherwise.
Return type:

bool

catlearn.ga.initialize

Function to initialize a population.

catlearn.ga.initialize.initialize_population(pop_size, dimension, dmax=None)

Generate a random starting population.

Parameters:
  • pop_size (int) – Population size.
  • d_param (int) – Dimension of parameters in model.

catlearn.ga.io

Functions to read and write GA data.

catlearn.ga.io.read_data(writefile)

Funtion to read population and fitness.

Parameters:writefile (str) – Name of the JSON file to read.
Returns:
  • population (array) – The population saved from a previous search.
  • fitness (array) – The fitness associated with the saved population.

catlearn.ga.mating

Cut and splice mating function.

catlearn.ga.mating.cut_and_splice(parent_one, parent_two, index='random')

Perform cut_and_splice between two parents.

Parameters:
  • parent_one (list) – List of params for first parent.
  • parent_two (list) – List of params for second parent.
  • index (str) – Define how to choose size of each cut index.
Returns:

offspring – A new child candidate from the two parents.
Return type:

array

catlearn.ga.mutate

Define some mutation functions.

catlearn.ga.mutate.probability_include(parent_one)

A mutation that will include features with a certain probability.

Parameters:parent_one (list) – List of params for first parent.
Returns:p1 – Mutated parameter list based on the parent parameters provided.
Return type:list
catlearn.ga.mutate.probability_remove(parent_one)

A mutation that will remove features with a certain probability.

Parameters:parent_one (list) – List of params for first parent.
Returns:p1 – Mutated parameter list based on the parent parameters provided.
Return type:list
catlearn.ga.mutate.random_permutation(parent_one)

Perform a random permutation on a parameter index.

Parameters:parent_one (list) – List of params for first parent.
Returns:p1 – Mutated parameter list based on the parent parameters provided.
Return type:list

catlearn.ga.natural_selection

Functions to perform some natural selection.

catlearn.ga.natural_selection.population_reduction(pop, fit, population_size)

Method to reduce population size to constant.

Parameters:
  • pop (list) – Extended population.
  • fit (list) – Extended fitness assignment.
  • population_size (int) – The population size.
  • pareto (bool) – Flag to specify whether search is for Pareto optimal set.
Returns:

  • population (list) – The population after natural selection.
  • fitness (list) – The fitness for the current population.
catlearn.ga.natural_selection.remove_duplicates(population, fitness, accuracy)

Function to delete duplicate candidates based on fitness.

Parameters:
  • population (array) – The current population.
  • fitness (array) – The fitness for the current population.
  • accuracy (int) – Number of decimal places to include when finding unique.
Returns:

  • population (list) – The population after duplicates deleted.
  • fitness (list) – The fitness for the population after duplicates deleted.

catlearn.ga.predictors

Some generic prediction functions.

catlearn.ga.predictors.minimize_error(train_features, train_targets, test_features, test_targets)

A generic fitness function.

This fitness function will minimize the cost function.

Parameters:
  • train_features (array) – The training features.
  • train_targets (array) – The training targets.
  • test_features (array) – The test feaatures.
  • test_targets (array) – The test targets.
catlearn.ga.predictors.minimize_error_descriptors(train_features, train_targets, test_features, test_targets)

A generic fitness function.

This fitness function will minimize the cost function as well as the number of descriptors. This will provide a Pareto optimial set of solutions upon convergence.

Parameters:
  • train_features (array) – The training features.
  • train_targets (array) – The training targets.
  • test_features (array) – The test feaatures.
  • test_targets (array) – The test targets.
catlearn.ga.predictors.minimize_error_time(train_features, train_targets, test_features, test_targets)

A generic fitness function.

This fitness function will minimize the cost function as well as the time to train the model. This will provide a Pareto optimial set of solutions upon convergence.

Parameters:
  • train_features (array) – The training features.
  • train_targets (array) – The training targets.
  • test_features (array) – The test feaatures.
  • test_targets (array) – The test targets.