culebra.trainer.ea.HomogeneousSequentialIslandsEA class

class HomogeneousSequentialIslandsEA(solution_cls: Type[Individual], species: Species, fitness_function: FitnessFunction, subtrainer_cls: Type[SinglePopEA], max_num_iters: int | None = None, custom_termination_func: Callable[[SinglePopEA], bool] | None = None, pop_size: int | None = None, crossover_func: Callable[[Individual, Individual], Tuple[Individual, Individual]] | None = None, mutation_func: Callable[[Individual, float], Tuple[Individual]] | None = None, selection_func: Callable[[List[Individual], int, Any], List[Individual]] | None = None, crossover_prob: float | None = None, mutation_prob: float | None = None, gene_ind_mutation_prob: float | None = None, selection_func_params: Dict[str, Any] | None = None, num_subtrainers: int | None = None, representation_size: int | None = None, representation_freq: int | None = None, representation_topology_func: Callable[[int, int, Any], List[int]] | None = None, representation_topology_func_params: Dict[str, Any] | None = None, representation_selection_func: Callable[[List[Individual], Any], Individual] | None = None, representation_selection_func_params: Dict[str, Any] | None = None, checkpoint_enable: bool | None = None, checkpoint_freq: int | None = None, checkpoint_filename: str | None = None, verbose: bool | None = None, random_seed: int | None = None, **subtrainer_params: Any)

Create a new trainer.

Parameters:
Raises:

  • TypeError – If any argument is not of the appropriate type

  • ValueError – If any argument has an incorrect value

Class attributes

HomogeneousSequentialIslandsEA.stats_names = ('Iter', 'Pop', 'NEvals')

Statistics calculated each iteration.

HomogeneousSequentialIslandsEA.objective_stats = {'Avg': <function mean>, 'Max': <function max>, 'Min': <function min>, 'Std': <function std>}

Statistics calculated for each objective.

Class methods

classmethod HomogeneousSequentialIslandsEA.load_pickle(filename: str) → Base

Load a pickled object from a file.

Parameters:

filename (str) – The file name.

Raises:

Properties

property HomogeneousSequentialIslandsEA.solution_cls: Type[Individual]

Get and set the individual class.

Getter:

Return the individual class

Setter:

Set a new individual class

Type:

An Individual subclass

Raises:

TypeError – If set to a value which is not an Individual subclass

property HomogeneousSequentialIslandsEA.species: Species

Get and set the species.

Getter:

Return the species

Setter:

Set a new species

Type:

Species

Raises:

TypeError – If set to a value which is not a Species instance

property HomogeneousSequentialIslandsEA.fitness_function: FitnessFunction

Get and set the training fitness function.

Getter:

Return the fitness function

Setter:

Set a new fitness function

Type:

FitnessFunction

Raises:

TypeError – If set to a value which is not a fitness function

property HomogeneousSequentialIslandsEA.subtrainer_cls: Type[SinglePopEA]

Get and set the trainer class to handle the subpopulations.

Each subpopulation will be handled by a single-population evolutionary trainer.

Getter:

Return the trainer class

Setter:

Set new trainer class

Type:

A SinglePopEA subclass

Raises:

TypeError – If set to a value which is not a SinglePopEA subclass

property HomogeneousSequentialIslandsEA.max_num_iters: int

Get and set the maximum number of iterations.

Getter:

Return the current maximum number of iterations

Setter:

Set a new value for the maximum number of iterations. If set to None, the default maximum number of iterations, DEFAULT_MAX_NUM_ITERS, is chosen

Type:

int

Raises:

  • TypeError – If set to a value which is not an integer

  • ValueError – If set to a value which is not a positive number

property HomogeneousSequentialIslandsEA.current_iter: int

Return the current iteration.

Type:

int

property HomogeneousSequentialIslandsEA.custom_termination_func: Callable[[Trainer], bool]

Get and set the custom termination criterion.

The custom termination criterion must be a function which receives the trainer as its unique argument and returns a boolean value, True if the search should terminate or False otherwise.

If more than one arguments are needed to define the termniation condition, functools.partial() can be used:

from functools import partial

def my_crit(trainer, max_iters):
    if trainer.current_iter < max_iters:
        return False
    return True

trainer.custom_termination_func = partial(my_crit, max_iters=10)
Getter:

Return the current custom termination criterion

Setter:

Set a new custom termination criterion. If set to None, the default termination criterion is used. Defaults to None

Type:

Callable

Raises:

TypeError – If set to a value which is not callable

property HomogeneousSequentialIslandsEA.pop_size: int

Get and set the population size.

Getter:

Return the current population size

Setter:

Set a new value for the population size. If set to None, DEFAULT_POP_SIZE is chosen

Type:

int, greater than zero

Raises:

  • TypeError – If set to a value which is not an int

  • ValueError – If set to a value which is not greater than zero

property HomogeneousSequentialIslandsEA.crossover_func: Callable[[Individual, Individual], Tuple[Individual, Individual]]

Get and set the crossover function.

Getter:

Return the current crossover function

Setter:

Set a new crossover function. If set to None, the crossover() method of the individual class evolved by the trainer is chosen

Type:

Callable

Raises:

TypeError – If set to a value which is not callable

property HomogeneousSequentialIslandsEA.mutation_func: Callable[[Individual, float], Tuple[Individual]]

Get and set the mutation function.

Getter:

Return the current mutation function

Setter:

Set a new mutation function. If set to None, the mutate() method of the individual class evolved by the trainer is chosen

Type:

Callable

Raises:

TypeError – If set to a value which is not callable

property HomogeneousSequentialIslandsEA.selection_func: Callable[[List[Individual], int, Any], List[Individual]]

Get and set the selection function.

Getter:

Return the current selection function

Setter:

Set the new selection function. If set to None, DEFAULT_SELECTION_FUNC is chosen

Type:

Callable

Raises:

TypeError – If set to a value which is not callable

property HomogeneousSequentialIslandsEA.crossover_prob: float

Get and set the crossover probability.

Getter:

Return the current crossover probability

Setter:

Set the new crossover probability. If set to None, DEFAULT_CROSSOVER_PROB is chosen

Type:

float in (0, 1)

Raises:

  • TypeError – If set to a value which is not a real number

  • ValueError – If set to a value which is not in (0, 1)

property HomogeneousSequentialIslandsEA.mutation_prob: float

Get and set the mutation probability.

Getter:

Return the current mutation probability

Setter:

Set the new mutation probability. If set to None, DEFAULT_MUTATION_PROB is chosen

Type:

float in (0, 1)

Raises:

  • TypeError – If set to a value which is not a real number

  • ValueError – If set to a value which is not in (0, 1)

property HomogeneousSequentialIslandsEA.gene_ind_mutation_prob: float

Get and set the gene independent mutation probability.

Getter:

Return the current gene independent mutation probability

Setter:

Set the new gene independent mutation probability. If set to None, DEFAULT_GENE_IND_MUTATION_PROB is chosen

Type:

float in (0, 1)

Raises:

  • TypeError – If set to a value which is not a real number

  • ValueError – If set to a value which is not in (0, 1)

property HomogeneousSequentialIslandsEA.selection_func_params: Dict[str, Any]

Get and set the parameters of the selection function.

Getter:

Return the current parameters for the selection function

Setter:

Set new parameters. If set to None, DEFAULT_SELECTION_FUNC_PARAMS is chosen

Type:

dict

Raises:

TypeError – If set to a value which is not a dict

property HomogeneousSequentialIslandsEA.num_subtrainers: int

Get and set the number of subtrainers.

Getter:

Return the current number of subtrainers

Setter:

Set a new value for the number of subtrainers. If set to None, DEFAULT_NUM_SUBTRAINERS is chosen

Type:

int

Raises:

  • TypeError – If set to a value which is not an integer

  • ValueError – If set to a value which is not a positive number

property HomogeneousSequentialIslandsEA.representation_size: int

Get and set the representation size.

The representation size is the number of representatives sent to the other subtrainers

Getter:

Return the current representation size

Setter:

Set the new representation size. If set to None, DEFAULT_REPRESENTATION_SIZE is chosen

Type:

int

Raises:

  • TypeError – If set to a value which is not an integer

  • ValueError – If set to a value which is not positive

property HomogeneousSequentialIslandsEA.representation_freq: int

Get and set the number of iterations between representatives sendings.

Getter:

Return the current frequency

Setter:

Set a new value for the frequency. If set to None, DEFAULT_REPRESENTATION_FREQ is chosen

Type:

int

Raises:

  • TypeError – If set to a value which is not an integer

  • ValueError – If set to a value which is not a positive number

property HomogeneousSequentialIslandsEA.representation_topology_func: Callable[[int, int, Any], List[int]]

Get and set the representation topology function.

Getter:

Return the representation topology function

Setter:

Set new representation topology function. If set to None, DEFAULT_ISLANDS_REPRESENTATION_TOPOLOGY_FUNC is chosen

Type:

Callable

Raises:

TypeError – If set to a value which is not callable

property HomogeneousSequentialIslandsEA.representation_topology_func_params: Dict[str, Any]

Get and set the parameters of the representation topology function.

Getter:

Return the current parameters for the representation topology function

Setter:

Set new parameters. If set to None, DEFAULT_ISLANDS_REPRESENTATION_TOPOLOGY_FUNC_PARAMS is chosen

Type:

dict

Raises:

TypeError – If set to a value which is not a dict

property HomogeneousSequentialIslandsEA.representation_selection_func: Callable[[List[Solution], Any], Solution]

Get and set the representation selection policy function.

The representation selection policy func chooses which solutions are selected as representatives of each subtrainer.

Getter:

Return the representation selection policy function

Setter:

Set new representation selection policy function. If set to None, DEFAULT_REPRESENTATION_SELECTION_FUNC is chosen

Type:

Callable

Raises:

TypeError – If set to a value which is not callable

property HomogeneousSequentialIslandsEA.representation_selection_func_params: Dict[str, Any]

Get and set the parameters of the representation selection function.

Getter:

Return the current parameters for the representation selection policy function

Setter:

Set new parameters. If set to None, DEFAULT_REPRESENTATION_SELECTION_FUNC_PARAMS is chosen

Type:

dict

Raises:

TypeError – If set to a value which is not a dict

property HomogeneousSequentialIslandsEA.checkpoint_enable: bool

Enable or disable checkpointing.

Getter:

Return True if checkpoinitng is enabled, or False otherwise

Setter:

New value for the checkpoint enablement. If set to None, DEFAULT_CHECKPOINT_ENABLE is chosen

Type:

bool

Raises:

TypeError – If set to a value which is not boolean

property HomogeneousSequentialIslandsEA.checkpoint_freq: int

Get and set the checkpoint frequency.

Getter:

Return the checkpoint frequency

Setter:

Set a value for the checkpoint frequency. If set to None, DEFAULT_CHECKPOINT_FREQ is chosen

Type:

int

Raises:

  • TypeError – If set to a value which is not an integer

  • ValueError – If set to a value which is not a positive number

property HomogeneousSequentialIslandsEA.checkpoint_filename: str

Get and set the checkpoint file path.

Getter:

Return the checkpoint file path

Setter:

Set a new value for the checkpoint file path. If set to None, DEFAULT_CHECKPOINT_FILENAME is chosen

Type:

str

Raises:
property HomogeneousSequentialIslandsEA.verbose: bool

Get and set the verbosity of this trainer.

Getter:

Return the verbosity

Setter:

Set a new value for the verbosity. If set to None, __debug__ is chosen

Type:

bool

Raises:

TypeError – If set to a value which is not boolean

property HomogeneousSequentialIslandsEA.random_seed: int

Get and set the initial random seed used by this trainer.

Getter:

Return the seed

Setter:

Set a new value for the random seed

Type:

int

property HomogeneousSequentialIslandsEA.logbook: Logbook | None

Get the training logbook.

Return a logbook with the statistics of the search or None if the search has not been done yet.

Type:

Logbook

property HomogeneousSequentialIslandsEA.num_evals: int | None

Get the number of evaluations performed while training.

Return the number of evaluations or None if the search has not been done yet.

Type:

int

property HomogeneousSequentialIslandsEA.runtime: float | None

Get the training runtime.

Return the training runtime or None if the search has not been done yet.

Type:

float

property HomogeneousSequentialIslandsEA.index: int

Get and set the trainer index.

The trainer index is only used by distributed trainers. For the rest of trainers DEFAULT_INDEX is used.

Getter:

Return the trainer index

Setter:

Set a new value for trainer index. If set to None, DEFAULT_INDEX is chosen

Type:

int

Raises:

  • TypeError – If set to a value which is not an integer

  • ValueError – If set to a value which is a negative number

property HomogeneousSequentialIslandsEA.container: Trainer | None

Get and set the container of this trainer.

The trainer container is only used by distributed trainers. For the rest of trainers defaults to None.

Getter:

Return the container

Setter:

Set a new value for container of this trainer

Type:

Trainer

Raises:

TypeError – If set to a value which is not a valid trainer

property HomogeneousSequentialIslandsEA.representatives: Sequence[Sequence[Solution | None]] | None

Return the representatives of the other species.

Only used by cooperative trainers. If the trainer does not use representatives, None is returned.

property HomogeneousSequentialIslandsEA.subtrainer_params: Dict[str, Any]

Get and set the custom parameters of the subtrainers.

Getter:

Return the current parameters for the subtrainers

Setter:

Set new parameters

Type:

dict

Raises:

TypeError – If set to a value which is not a dict

property HomogeneousSequentialIslandsEA.subtrainer_checkpoint_filenames: Generator[str, None, None]

Checkpoint file name of all the subtrainers.

property HomogeneousSequentialIslandsEA.subtrainers: List[SingleSpeciesTrainer] | None

Return the subtrainers.

One single-species trainer for each subtrainer

Type:

list of SingleSpeciesTrainer trainers

Private properties

property HomogeneousSequentialIslandsEA._subtrainer_suffixes: Generator[str, None, None]

Return the suffixes for the different subtrainers.

Can be used to generate the subtrainers’ names, checkpoint files, etc.

Returns:

A generator of the suffixes

Return type:

A generator of str

Static methods

static HomogeneousSequentialIslandsEA.receive_representatives(subtrainer) → None

Receive representative solutions.

Parameters:

subtrainer (SingleSpeciesTrainer) – The subtrainer receiving representatives

static HomogeneousSequentialIslandsEA.send_representatives(subtrainer) → None

Send representatives.

Parameters:

subtrainer (SingleSpeciesTrainer) – The sender subtrainer

Methods

HomogeneousSequentialIslandsEA.save_pickle(filename: str) → None

Pickle this object and save it to a file.

Parameters:

filename (str) – The file name.

Raises:
HomogeneousSequentialIslandsEA.reset() → None

Reset the trainer.

Delete the state of the trainer (with _reset_state()) and also all the internal data structures needed to perform the search (with _reset_internals()).

This method should be invoqued each time a hyper parameter is modified.

HomogeneousSequentialIslandsEA.evaluate(sol: Solution, fitness_func: FitnessFunction | None = None, index: int | None = None, representatives: Sequence[Sequence[Solution | None]] | None = None) → None

Evaluate one solution.

Its fitness will be modified according with the fitness function results. Besides, if called during training, the number of evaluations will be also updated.

Parameters:

  • sol (Solution) – The solution

  • fitness_func (FitnessFunction, optional) – The fitness function. If omitted, the default training fitness function (fitness_function) is used

  • index (int, optional) – Index where sol should be inserted in the representatives sequence to form a complete solution for the problem. If omitted, index is used

  • representatives (Sequence of Sequence of Solution or None, optional) – Sequence of representatives of other species or None (if no representatives are needed to evaluate sol). If omitted, the current value of representatives is used

HomogeneousSequentialIslandsEA.best_solutions() → Sequence[HallOfFame]

Get the best solutions found for each species.

Return the best single solution found for each species

Returns:

A list containing HallOfFame of solutions. One hof for each species

Return type:

list of HallOfFame

HomogeneousSequentialIslandsEA.best_representatives() → List[List[Solution]] | None

Return a list of representatives from each species.

Only used for cooperative trainers.

Returns:

A list of representatives lists if the trainer is cooperative or None in other cases.

Return type:

list of list of Solution or None

HomogeneousSequentialIslandsEA.train(state_proxy: DictProxy | None = None) → None

Perform the training process.

Parameters:

state_proxy (DictProxy, optional) – Dictionary proxy to copy the output state of the trainer procedure. Only used if train is executed within a multiprocessing.Process. Defaults to None

HomogeneousSequentialIslandsEA.test(best_found: Sequence[HallOfFame], fitness_func: FitnessFunction | None = None, representatives: Sequence[Sequence[Solution]] | None = None) → None

Apply the test fitness function to the solutions found.

Update the solutions in best_found with their test fitness.

Parameters:
Raises:

  • TypeError – If any parameter has a wrong type

  • ValueError – If any parameter has an invalid value.

Private methods

HomogeneousSequentialIslandsEA._generate_subtrainers() → None

Generate the subpopulation trainers.

Also assign an index and a container to each subpopulation SinglePopEA trainer, change the subpopulation trainers’ checkpoint_filename according to the container checkpointing file name and each subpopulation index.

Finally, the _preprocess_iteration() and _postprocess_iteration() methods of the subtrainer_cls class are dynamically overridden, in order to allow individuals exchange between subpopulation trainers, if necessary

HomogeneousSequentialIslandsEA._get_state() → Dict[str, Any]

Return the state of this trainer.

Default state is a dictionary composed of the values of the logbook, num_evals, runtime, current_iter, and representatives trainer properties, along with a private boolean attribute that informs if the search has finished and also the states of the random and numpy.random modules.

If subclasses use any more properties to keep their state, the _get_state() and _set_state() methods must be overridden to take into account such properties.

Type:

dict

HomogeneousSequentialIslandsEA._set_state(state: Dict[str, Any]) → None

Set the state of this trainer.

If subclasses use any more properties to keep their state, the _get_state() and _set_state() methods must be overridden to take into account such properties.

Parameters:

state (dict) – The last loaded state

HomogeneousSequentialIslandsEA._save_state() → None

Save the state at a new checkpoint.

Overridden to call also the _save_state() method of each subtrainer.

Raises:

Exception – If the checkpoint file can’t be written

HomogeneousSequentialIslandsEA._load_state() → None

Load the state of the last checkpoint.

Overridden to call also the _load_state() method of each subtrainer.

Raises:

Exception – If the checkpoint file can’t be loaded

HomogeneousSequentialIslandsEA._new_state() → None

Generate a new trainer state.

Overridden to call also the _new_state() method of each subtrainer.

HomogeneousSequentialIslandsEA._init_state() → None

Init the trainer state.

If there is any checkpoint file, the state is initialized from it with the _load_state() method. Otherwise a new initial state is generated with the _new_state() method.

HomogeneousSequentialIslandsEA._reset_state() → None

Reset the trainer state.

If subclasses overwrite the _new_state() method to add any new property to keep their state, this method should also be overridden to reset the full state of the trainer.

HomogeneousSequentialIslandsEA._init_internals() → None

Set up the trainer internal data structures to start searching.

Overridden to create the subtrainers and communication queues.

HomogeneousSequentialIslandsEA._reset_internals() → None

Reset the internal structures of the trainer.

Overridden to reset the subtrainers and communication queues.

Init the search process.

Initialize the state of the trainer (with _init_state()) and all the internal data structures needed (with _init_internals()) to perform the search.

Apply the search algorithm.

Execute the trainer until the termination condition is met. Each iteration is composed by the following steps:

Finish the search process.

Overridden to call also the _finish_search() method of each subtrainer.

HomogeneousSequentialIslandsEA._start_iteration() → None

Start an iteration.

Prepare the metrics before each iteration is run. Overridden to call also the _start_iteration() method of each subtrainer.

HomogeneousSequentialIslandsEA._preprocess_iteration() → None

Preprocess the iteration of all the subtrainers.

Overridden to call also the _preprocess_iteration() method of each subtrainer.

HomogeneousSequentialIslandsEA._do_iteration() → None

Implement an iteration of the search process.

Overridden to call also the _do_iteration() method of each subtrainer.

HomogeneousSequentialIslandsEA._postprocess_iteration() → None

Postprocess the iteration of all the subtrainers.

Overridden to call also the _postprocess_iteration() method of each subtrainer.

HomogeneousSequentialIslandsEA._finish_iteration() → None

Finish an iteration.

Close the metrics after each iteration is run. Overridden to call also the _finish_iteration() method of each subtrainer and accumulate the current number of evaluations of all the subtrainers.

HomogeneousSequentialIslandsEA._do_iteration_stats() → None

Perform the iteration stats.

Overridden to call also the _do_iteration_stats() method of each subtrainer.

HomogeneousSequentialIslandsEA._default_termination_func() → bool

Set the default termination criterion.

Return True if max_num_iters iterations have been run.

HomogeneousSequentialIslandsEA._termination_criterion() → bool

Return true if the search should terminate.

Returns True if either the default termination criterion or a custom termination criterion is met. The default termination criterion is implemented by the _default_termination_func() method. Another custom termination criterion can be set with custom_termination_func method.

HomogeneousSequentialIslandsEA._init_representatives() → None

Init the representatives of the other species.

Only used for cooperative approaches, which need representatives of all the species to form a complete solution for the problem. Cooperative subclasses of the Trainer class should override this method to get the representatives of the other species initialized.