`culebra.trainer.ea.abc.HomogeneousIslandsEA` class¶

class HomogeneousIslandsEA(solution_cls: type[Individual], species: Species, fitness_function: FitnessFunction, subtrainer_cls: type[SinglePopEA], max_num_iters: int | None = None, custom_termination_func: Callable[[HomogeneousIslandsEA], 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_activation: bool | None = None, checkpoint_freq: int | None = None, checkpoint_filename: str | None = None, verbosity: bool | None = None, random_seed: int | None = None, **subtrainer_params: Any)¶

Bases: IslandsEA, HomogeneousEA

Create a new trainer.

Parameters:

solution_cls (type[Individual]) – The individual class
species (Species) – The species for all the individuals
fitness_function (FitnessFunction) – The training fitness function
subtrainer_cls (type[SinglePopEA]) – Single-population EA trainer class to handle the subpopulations
max_num_iters (int) – Maximum number of iterations. If omitted, _default_max_num_iters will be used. Defaults to None
custom_termination_func (Callable) – Custom termination criterion. If omitted, _default_termination_func() is used. Defaults to None
pop_size (int) – The population size. If omitted, _default_pop_size is used. Defaults to None
crossover_func (Callable) – The crossover function. If omitted, _default_crossover_func is used. Defaults to None
mutation_func (Callable) – The mutation function. If omitted, _default_mutation_func is used. Defaults to None
selection_func (Callable) – The selection function. If omitted, _default_selection_func is used. Defaults to None
crossover_prob (float) – The crossover probability. If omitted, _default_crossover_prob is used. Defaults to None
mutation_prob (float) – The mutation probability. If omitted, _default_mutation_prob is used. Defaults to None
gene_ind_mutation_prob (float) – The gene independent mutation probability. If omitted, _default_gene_ind_mutation_prob is used. Defaults to None
selection_func_params (dict) – The parameters for the selection function. If omitted, _default_selection_func_params is used. Defaults to None
num_subtrainers (int) – The number of subpopulations. If omitted, _default_num_subtrainers will be used. Defaults to None
representation_size (int) – Number of representative individuals that will be sent to the other subpopulations. If omitted, _default_representation_size will be used. Defaults to None
representation_freq (int) – Number of iterations between representatives sendings. If omitted, _default_representation_freq will be used. Defaults to None
representation_topology_func (Callable) – Topology function for representatives sending. If omitted, _default_representation_topology_func will be used. Defaults to None
representation_topology_func_params (dict) – Parameters to obtain the destinations with the topology function. If omitted, _default_representation_topology_func_params will be used. Defaults to None
representation_selection_func (Callable) – Policy function to choose the representatives from each subpopulation. If omitted, _default_representation_selection_func will be used. Defaults to None
representation_selection_func_params (dict) – Parameters to obtain the representatives with the selection policy function. If omitted, _default_representation_selection_func_params will be used. Defaults to None
checkpoint_activation (bool) – Checkpoining activation. If omitted, _default_checkpoint_activation will be used. Defaults to None
checkpoint_freq (int) – The checkpoint frequency. If omitted, _default_checkpoint_freq will be used. Defaults to None
checkpoint_filename (str) – The checkpoint file path. If omitted, _default_checkpoint_filename will be used. Defaults to None
verbosity (bool) – The verbosity. If omitted, _default_verbosity will be used. Defaults to None
random_seed (int) – The seed. Defaults to None
subtrainer_params (dict) – Custom parameters for the subpopulations trainer

Raises:

TypeError – If any argument is not of the appropriate type
ValueError – If any argument has an incorrect value

Class attributes¶

HomogeneousIslandsEA.objective_stats = {'Avg': <function mean>, 'Max': <function max>, 'Min': <function min>, 'Std': <function std>}¶: Statistics calculated for each objective.

HomogeneousIslandsEA.stats_names = ('Iter', 'Pop', 'NEvals')¶: Statistics calculated each iteration.

Class methods¶

classmethod HomogeneousIslandsEA.load(filename: str) → Base¶

Load a serialized object from a file.

Parameters:

filename (str) – The file name.

Returns:

The loaded object

Raises:

TypeError – If filename is not a valid file name
ValueError – If the filename extension is not SERIALIZED_FILE_EXTENSION

Properties¶

property HomogeneousIslandsEA.checkpoint_activation: bool¶

Checkpointing activation.

Returns:: True if checkpointing is active, or False otherwise
Return type:: bool
Setter:: Modify the checkpointing activation
Parameters:: value (bool) – New value for the checkpoint activation. If set to None, _default_checkpoint_activation is chosen
Raises:: TypeError – If value is not a boolean value

property HomogeneousIslandsEA.checkpoint_filename: str¶

Checkpoint file path.

Return type:

str

Setter:

Modify the checkpoint file path

Parameters:

value (str) – New value for the checkpoint file path. If set to None, _default_checkpoint_filename is chosen

Raises:

TypeError – If value is not a valid file name
ValueError – If the value extension is not SERIALIZED_FILE_EXTENSION

property HomogeneousIslandsEA.checkpoint_freq: int¶

Checkpoint frequency.

Return type:

int

Setter:

Modify the checkpoint frequency

Parameters:

value (int) – New value for the checkpoint frequency. If set to None, _default_checkpoint_freq is chosen

Raises:

TypeError – If value is not an integer
ValueError – If value is not a positive number

property HomogeneousIslandsEA.container: Trainer | None¶

Container of this trainer.

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

Return type:: Trainer
Setter:: Set a new value for container of this trainer
Parameters:: value (Trainer) – New value for the container or None
Raises:: TypeError – If value is not a valid trainer

property HomogeneousIslandsEA.crossover_func: Callable[[Individual, Individual], tuple[Individual, Individual]]¶

Crossover function.

Returns:: If subtrainers have been generated, the subtrainers crossover function. Otherwise, the crossover function value used to call the constructor
Return type:: Callable
Setter:: Set a new crossover function
Parameters:: func (Callable) – The new crossover function. If omitted, _default_crossover_func is chosen
Raises:: TypeError – If func is not callable

property HomogeneousIslandsEA.crossover_prob: float¶

Crossover probability.

Returns:

If subtrainers have been generated, the subtrainers crossover probability. Otherwise, the crossover probability value used to call the constructor

Return type:

float

Setter:

Set a new crossover probability

Parameters:

prob (float) – The new probability. If omitted, _default_crossover_prob is chosen

Raises:

TypeError – If prob is not a real number
ValueError – If prob is not in (0, 1)

property HomogeneousIslandsEA.current_iter: int | None¶

Current iteration.

Returns:: The current iteration or None if the search has not been done yet
Return type:: int

property HomogeneousIslandsEA.custom_termination_func: Callable[[Trainer], bool] | None¶

Custom termination criterion.

Although the trainer will always stop when the max_num_iters are reached, a custom termination criterion can be set to detect convergente and stop the trainer earlier. This 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 termination 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)

Setter:: Set a new custom termination criterion
Parameters:: func (Callable) – The new custom termination criterion. If set to None, the default termination criterion is used
Raises:: TypeError – If func is not callable

property HomogeneousIslandsEA.fitness_function: FitnessFunction¶

Training fitness function.

Return type:: FitnessFunction
Setter:: Set a new fitness function
Parameters:: func (FitnessFunction) – The new training fitness function
Raises:: TypeError – If func is not a valid fitness function

property HomogeneousIslandsEA.gene_ind_mutation_prob: float¶

Gene independent mutation probability.

Returns:

If subtrainers have been generated, the subtrainers gene independent mutation probability. Otherwise, the gene independent mutation probability value used to call the constructor

Return type:

float

Setter:

Set a new gene independent mutation probability

Parameters:

prob (float) – The new probability. If omitted, _default_gene_ind_mutation_prob is chosen

Raises:

TypeError – If prob is not a real number
ValueError – If prob is not in (0, 1)

property HomogeneousIslandsEA.index: int¶

Trainer index.

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

Return type:

int

Setter:

Set a new value for trainer index.

Parameters:

value (int) – New value for the trainer index. If set to None, _default_index is chosen

Raises:

TypeError – If value is not an integer
ValueError – If value is a negative number

property HomogeneousIslandsEA.logbook: Logbook | None¶

Trainer logbook.

Returns:: A logbook with the statistics of the search or None if the search has not been done yet
Return type:: Logbook

property HomogeneousIslandsEA.max_num_iters: int¶

Maximum number of iterations.

Return type:

int

Setter:

Set a new value for the maximum number of iterations

Parameters:

value (int) – The new maximum number of iterations. If set to None, the default maximum number of iterations, _default_max_num_iters, is chosen

Raises:

TypeError – If value is not an integer
ValueError – If value is not a positive number

property HomogeneousIslandsEA.mutation_func: Callable[[Individual, float], tuple[Individual]]¶

Mutation function.

Returns:: If subtrainers have been generated, the subtrainers mutation function. Otherwise, the mutation function value used to call the constructor
Return type:: Callable
Setter:: Set a new mutation function
Parameters:: func (Callable) – The new mutation function. If omitted, _default_mutation_func is chosen
Raises:: TypeError – If func is not callable

property HomogeneousIslandsEA.mutation_prob: float¶

Mutation probability.

Returns:

If subtrainers have been generated, the subtrainers mutation probability. Otherwise, the mutation probability value used to call the constructor

Return type:

float

Setter:

Set a new mutation probability

Parameters:

prob (float) – The new probability. If omitted, _default_mutation_prob is chosen

Raises:

TypeError – If prob is not a real number
ValueError – If prob is not in (0, 1)

property HomogeneousIslandsEA.num_evals: int | None¶

Number of evaluations performed while training.

Returns:: The number of evaluations or None if the search has not been done yet
Return type:: int

property HomogeneousIslandsEA.num_subtrainers: int¶

Number of subtrainers.

Return type:

int

Setter:

Set a new value for the number of subtrainers

Parameters:

value (int) – The new number of subtrainers. If set to None, _default_num_subtrainers is chosen

Raises:

TypeError – If value is not an integer
ValueError – If value is not a positive number

property HomogeneousIslandsEA.pop_size: int¶

Population size.

Returns:

If subtrainers have been generated, the subtrainers population size. Otherwise, the population size value used to call the constructor

Return type:

int

Setter:

Set a new population size

Parameters:

size (int) – The new population size. If omitted, _default_pop_size is chosen

Raises:

TypeError – If size is not an int
ValueError – If size is not greater than zero

property HomogeneousIslandsEA.random_seed: int¶

Random seed used by this trainer.

Return type:: int
Setter:: Set a new value for the random seed
Parameters:: value (int) – New value

property HomogeneousIslandsEA.representation_freq: int¶

Number of iterations between representatives sendings.

Return type:

int

Setter:

Set a new value for the frequency

Parameters:

value (int) – The new frequency. If set to None, _default_representation_freq is chosen

Raises:

TypeError – If value is not an integer
ValueError – If value is not a positive number

property HomogeneousIslandsEA.representation_selection_func: Callable[[list[Solution], Any], Solution]¶

Representation selection policy function.

Returns:: A function that chooses which solutions are selected as representatives of each subtrainer
Return type:: Callable
Setter:: Set new representation selection policy function.
Parameters:: func (Callable) – The new function. If set to None, _default_representation_selection_func is chosen
Raises:: TypeError – If func is not callable

property HomogeneousIslandsEA.representation_selection_func_params: dict[str, Any]¶

Parameters of the representation selection function.

Return type:: dict
Setter:: Set new parameters
Parameters:: params (dict) – The new parameters. If set to None, _default_representation_selection_func_params is chosen
Raises:: TypeError – If params is not a dict

property HomogeneousIslandsEA.representation_size: int¶

Representation size.

Returns:

The number of representatives sent to the other subtrainers

Return type:

int

Setter:

Set a new representation size

Parameters:

size (int) – The new size. If set to None, _default_representation_size is chosen

Raises:

TypeError – If size is not an integer number
ValueError – If size is not positive

property HomogeneousIslandsEA.representation_topology_func: Callable[[int, int, Any], list[int]]¶

Representation topology function.

Return type:: Callable
Setter:: Set new representation topology function
Parameters:: func (Callable) – The new function. If set to None, _default_representation_topology_func is chosen
Raises:: TypeError – If func is not callable

property HomogeneousIslandsEA.representation_topology_func_params: dict[str, Any]¶

Parameters of the representation topology function.

Return type:: dict
Setter:: Set new parameters
Parameters:: params (dict) – The new parameters. If set to None, _default_representation_topology_func_params is chosen
Raises:: TypeError – If params is not a dict

property HomogeneousIslandsEA.representatives: list[list[Solution | None]] | None¶

Representatives of the other species.

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

Return type:: list[list[Solution]]

property HomogeneousIslandsEA.runtime: float | None¶

Training runtime.

Returns:: The training runtime or None if the search has not been done yet.
Return type:: float

property HomogeneousIslandsEA.selection_func: Callable[[list[Individual], int, Any], list[Individual]]¶

Selection function.

Returns:: If subtrainers have been generated, the subtrainers selection function. Otherwise, the selection function value used to call the constructor
Return type:: Callable
Setter:: Set a new selection function
Parameters:: func (Callable) – The new selection function. If omitted, _default_selection_func is chosen
Raises:: TypeError – If func is not callable

property HomogeneousIslandsEA.selection_func_params: dict[str, Any]¶

Parameters of the selection function.

Returns:: If subtrainers have been generated, the subtrainers parameters for the selection function. Otherwise, the parameters for the selection function used to call the constructor
Return type:: dict
Setter:: Set new parameters for the selection function
Parameters:: params – The new parameters. If omitted, _default_selection_func_params is chosen
Raises:: TypeError – If params is not a dict

property HomogeneousIslandsEA.solution_cls: type[Solution]¶

Solution class.

Return type:: type[Solution]
Setter:: Set a new solution class
Parameters:: cls (type[Solution]) – The new class
Raises:: TypeError – If cls is not valid solution class

property HomogeneousIslandsEA.species: Species¶

Species.

Return type:: Species
Setter:: Set a new species
Parameters:: value (Species) – The new species
Raises:: TypeError – If value is not a valid species

property HomogeneousIslandsEA.subtrainer_checkpoint_filenames: Generator[str, None, None]¶

Checkpoint file name for all the subtrainers.

Returns:: A generator of the filenames
Return type:: Generator[str, None, None]

property HomogeneousIslandsEA.subtrainer_cls: type[SingleSpeciesTrainer]¶

Trainer class to handle the subtrainers.

Each subtrainer will be handled by a single-species trainer.

Return type:: type[SingleSpeciesTrainer]
Setter:: Set a new trainer class to handle the subtrainers
Parameters:: cls (type[SingleSpeciesTrainer]) – The new class
Raises:: TypeError – If cls is not a valid trainer class

property HomogeneousIslandsEA.subtrainer_params: dict[str, Any]¶

Custom parameters for the subtrainers.

Return type:: dict
Setter:: Set new parameters
Parameters:: params (dict) – The new parameters
Raises:: TypeError – If params is not a dict

property HomogeneousIslandsEA.subtrainers: list[SingleSpeciesTrainer] | None¶

Subtrainers.

One single-species trainer for each subtrainer.

Return type:: list[SingleSpeciesTrainer]

property HomogeneousIslandsEA.verbosity: bool¶

Verbosity of this trainer.

Return type:: bool
Setter:: Set a new value for the verbosity
Parameters:: value (bool) – The verbosity. If set to None, _default_verbosity is chosen
Raises:: TypeError – If value is not boolean

Private properties¶

property HomogeneousIslandsEA._default_checkpoint_activation: bool¶

Default checkpointing activation.

Returns:: DEFAULT_CHECKPOINT_ACTIVATION
Return type:: bool

property HomogeneousIslandsEA._default_checkpoint_filename: str¶

Default checkpointing file name.

Returns:: DEFAULT_CHECKPOINT_FILENAME
Return type:: str

property HomogeneousIslandsEA._default_checkpoint_freq: int¶

Default checkpointing frequency.

Returns:: DEFAULT_CHECKPOINT_FREQ
Return type:: int

property HomogeneousIslandsEA._default_crossover_func: Callable[[Individual, Individual], tuple[Individual, Individual]]¶

Default crossover function.

Returns:: None to allow subtrainers use their default value
Return type:: Callable

property HomogeneousIslandsEA._default_crossover_prob: float¶

Default crossover probability.

Returns:: None to allow subtrainers use their default value
Return type:: float

property HomogeneousIslandsEA._default_gene_ind_mutation_prob: float¶

Default gene independent mutation probability.

Returns:: None to allow subtrainers use their default value
Return type:: float

property HomogeneousIslandsEA._default_index: int¶

Default index.

Returns:: DEFAULT_INDEX
Return type:: int

property HomogeneousIslandsEA._default_max_num_iters: int¶

Default maximum number of iterations.

Returns:: DEFAULT_MAX_NUM_ITERS
Return type:: int

property HomogeneousIslandsEA._default_mutation_func: Callable[[Individual, float], tuple[Individual]]¶

Default mutation function.

Returns:: None to allow subtrainers use their default value
Return type:: Callable

property HomogeneousIslandsEA._default_mutation_prob: float¶

Default mutation probability.

Returns:: None to allow subtrainers use their default value
Return type:: float

property HomogeneousIslandsEA._default_num_subtrainers: int¶

Default number of subtrainers.

Returns:: DEFAULT_NUM_SUBTRAINERS
Return type:: int

property HomogeneousIslandsEA._default_pop_size: int¶

Default population size.

Returns:: None to allow subtrainers use their default value
Return type:: int

property HomogeneousIslandsEA._default_representation_freq: int¶

Default number of iterations between representatives sending.

Returns:: DEFAULT_REPRESENTATION_FREQ
Return type:: int

property HomogeneousIslandsEA._default_representation_selection_func: Callable[[list[Solution], Any], Solution]¶

Default selection policy function to choose the representatives.

Returns:: DEFAULT_REPRESENTATION_SELECTION_FUNC
Return type:: Callable

property HomogeneousIslandsEA._default_representation_selection_func_params: dict[str, Any]¶

Default parameters for the representatives selection policy function.

Returns:: DEFAULT_REPRESENTATION_SELECTION_FUNC_PARAMS
Return type:: dict

property HomogeneousIslandsEA._default_representation_size: int¶

Default number of representatives sent to the other subtrainers.

Returns:: DEFAULT_REPRESENTATION_SIZE
Return type:: int

property HomogeneousIslandsEA._default_representation_topology_func: Callable[[int, int, Any], list[int]]¶

Default topology function.

Returns:: DEFAULT_ISLANDS_REPRESENTATION_TOPOLOGY_FUNC
Return type:: Callable

property HomogeneousIslandsEA._default_representation_topology_func_params: dict[str, Any]¶

Default parameters for the default topology function.

Returns:: DEFAULT_ISLANDS_REPRESENTATION_TOPOLOGY_FUNC_PARAMS
Return type:: dict

property HomogeneousIslandsEA._default_selection_func: Callable[[list[Individual], int, Any], list[Individual]]¶

Default selection function.

Returns:: None to allow subtrainers use their default value
Return type:: Callable

property HomogeneousIslandsEA._default_selection_func_params: dict[str, Any]¶

Parameters of the default selection function.

Returns:: None to allow subtrainers use their default value
Return type:: float

property HomogeneousIslandsEA._default_verbosity: bool¶

Default verbosity.

Returns:: DEFAULT_VERBOSITY
Return type:: bool

property HomogeneousIslandsEA._subtrainer_suffixes: Generator[str, None, None]¶

Suffixes for the different subtrainers.

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

Returns:: A generator of the suffixes
Return type:: Generator[str, None, None]

Static methods¶

static HomogeneousIslandsEA.receive_representatives(subtrainer: SinglePopEA) → None¶

Receive representative solutions.

Parameters:: subtrainer (SinglePopEA) – The subtrainer receiving representatives

static HomogeneousIslandsEA.send_representatives(subtrainer: SinglePopEA) → None¶

Send representatives.

Parameters:: subtrainer (SinglePopEA) – The sender subtrainer

Methods¶

HomogeneousIslandsEA.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[list[Solution]]

HomogeneousIslandsEA.best_solutions() → tuple[HallOfFame]¶

Get the best solutions found for each species.

Returns:: One Hall of Fame for each species
Return type:: tuple[HallOfFame]

HomogeneousIslandsEA.dump(filename: str) → None¶

Serialize this object and save it to a file.

Parameters:

filename (str) – The file name.

Raises:

TypeError – If filename is not a valid file name
ValueError – If the filename extension is not SERIALIZED_FILE_EXTENSION

HomogeneousIslandsEA.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) – The fitness function. If omitted, the default training fitness function (fitness_function) is used
index (int) – 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[Sequence[Solution]]) – 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

HomogeneousIslandsEA.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.

HomogeneousIslandsEA.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:

best_found (Sequence[HallOfFame]) – The best solutions found for each species. One HallOfFame for each species
fitness_func (FitnessFunction) – Fitness function used to evaluate the final solutions. If ommited, the default training fitness function (fitness_function) will be used
representatives (Sequence[Sequence[Solution]]) – Sequence of representatives of other species or None (if no representatives are needed). If omitted, the current value of representatives is used

Raises:

TypeError – If any parameter has a wrong type
ValueError – If any parameter has an invalid value.

HomogeneousIslandsEA.train(state_proxy: DictProxy | None = None) → None¶

Perform the training process.

Parameters:: state_proxy (DictProxy) – dictionary proxy to copy the output state of the trainer procedure. Only used if train is executed within a multiprocess.Process. Defaults to None

Private methods¶

HomogeneousIslandsEA._default_termination_func() → bool¶

Default termination criterion.

Returns:: True if max_num_iters iterations have been run
Return type:: bool

abstract HomogeneousIslandsEA._do_iteration() → None¶

Implement an iteration of the search process.

This abstract method should be implemented by subclasses in order to implement the desired behavior.

HomogeneousIslandsEA._do_iteration_stats() → None¶

Perform the iteration stats.

This method should be implemented by subclasses in order to perform the adequate stats.

HomogeneousIslandsEA._finish_iteration() → None¶

Finish an iteration.

Finish the iteration metrics (number of evaluations, execution time) after each iteration is run.

HomogeneousIslandsEA._finish_search() → None¶

Finish the search process.

This method is called after the search has finished. It can be overridden to perform any treatment of the solutions found.

HomogeneousIslandsEA._generate_subtrainers() → None¶

Generate the subpopulation trainers.

Also assign an index and a container to each SinglePopEA subtrainer, and change the subpopulation subtrainers’ 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 subtrainers, if necessary.

HomogeneousIslandsEA._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.

Return type:: dict

HomogeneousIslandsEA._init_internals() → None¶

Set up the trainer internal data structures to start searching.

Overridden to create the subtrainers and communication queues.

HomogeneousIslandsEA._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.

HomogeneousIslandsEA._init_search() → None¶

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.

HomogeneousIslandsEA._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.

HomogeneousIslandsEA._load_state() → None¶

Load the state of the last checkpoint.

Raises:: Exception – If the checkpoint file can’t be loaded

HomogeneousIslandsEA._new_state() → None¶

Generate a new trainer state.

Overridden to set the logbook to None, since the final logbook will be generated from the subtrainers’ logbook, once the trainer has finished.

HomogeneousIslandsEA._postprocess_iteration() → None¶

Postprocess after doing the iteration.

Subclasses should override this method to make any postprocessment after performing an iteration.

HomogeneousIslandsEA._preprocess_iteration() → None¶

Preprocess before doing the iteration.

Subclasses should override this method to make any preprocessment before performing an iteration.

HomogeneousIslandsEA._reset_internals() → None¶

Reset the internal structures of the trainer.

Overridden to reset the subtrainers and communication queues.

HomogeneousIslandsEA._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.

HomogeneousIslandsEA._save_state() → None¶

Save the state at a new checkpoint.

Raises:: Exception – If the checkpoint file can’t be written

HomogeneousIslandsEA._search() → None¶

Apply the search algorithm.

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

HomogeneousIslandsEA._set_cooperative_fitness(sol: Solution, fitness_trials_values: [Sequence[tuple[float]]]) → None¶

Estimate a solution fitness from multiple evaluation trials.

Applies an average of the fitness trials values. Trainers requiring another estimation should override this method.

Parameters:

sol (Solution) – The solution
fitness_trials_values (Sequence[tuple[float]]) – Sequence of fitness trials values. Each trial should be obtained with a different context in a cooperative trainer approach.

HomogeneousIslandsEA._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

HomogeneousIslandsEA._start_iteration() → None¶

Start an iteration.

Prepare the iteration metrics (number of evaluations, execution time) before each iteration is run.

HomogeneousIslandsEA._termination_criterion() → bool¶

Control the search termination.

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.
Return type:: bool

culebra.trainer.ea.abc.HomogeneousIslandsEA class¶

Class attributes¶

Class methods¶

Properties¶

Private properties¶

Static methods¶

Methods¶

Private methods¶

`culebra.trainer.ea.abc.HomogeneousIslandsEA` class¶