Parameter classes

class golem.core.optimisers.optimizer.AlgorithmParameters(multi_objective: bool = False, offspring_rate: float = 0.5, pop_size: int = 20, max_pop_size: Optional[int] = 55, adaptive_depth: bool = False, adaptive_depth_max_stagnation: int = 3, structural_diversity_frequency_check: int = 5)

Bases: object

Base class for definition of optimizers-specific parameters. Can be extended for custom optimizers.

Parameters

• multi_objective – defines if the optimizer must be multi-criterial

• offspring_rate – offspring rate used on next population

• pop_size – initial population size

• max_pop_size – maximum population size; optional, if unspecified, then population size is unbound

• adaptive_depth – flag to enable adaptive configuration of graph depth

• adaptive_depth_max_stagnation – max number of stagnating populations before adaptive depth increment

multi_objective: bool = False

offspring_rate: float = 0.5

pop_size: int = 20

max_pop_size: Optional[int] = 55

adaptive_depth: bool = False

adaptive_depth_max_stagnation: int = 3

structural_diversity_frequency_check: int = 5

class golem.core.optimisers.optimization_parameters.OptimizationParameters(num_of_generations: Optional[int] = None, timeout: Optional[datetime.timedelta] = datetime.timedelta(seconds=300), early_stopping_iterations: Optional[int] = 50, early_stopping_timeout: Optional[float] = 5, keep_n_best: int = 1, max_graph_fit_time: Optional[datetime.timedelta] = None, n_jobs: int = 1, show_progress: bool = True, collect_intermediate_metric: bool = False, parallelization_mode: str = 'populational', static_individual_metadata: dict = <factory>, keep_history: bool = True, history_dir: Optional[str] = <factory>)

Bases: object

Defines general algorithm-independent parameters of the composition process (like stop condition, validation, timeout, logging etc.)

Options related to stop condition:

Parameters

• num_of_generations – maximum number of optimizer generations

• timeout – max time in minutes available for composition process

• early_stopping_iterations –

  for early stopping.

  Optional max number of stagnating iterations for early stopping. If both early_stopping options are None, then do not use early stopping.

• early_stopping_timeout –

  for early stopping.

  Optional duration (in minutes) of stagnating optimization for early stopping. If both early_stopping options are None, then do not use early stopping.

Infrastructure options (logging, performance)

Parameters

• keep_n_best – number of the best individuals of previous generation to keep in next generation

• max_graph_fit_time – time constraint for evaluation of each graph (datetime.timedelta)

• n_jobs – num of n_jobs

• show_progress – bool indicating whether to show progress using tqdm or not

• collect_intermediate_metric – save metrics for intermediate (non-root) nodes in graph

• parallelization_mode – identifies the way to parallelize population evaluation

History options:

Parameters

• keep_history – if True, then save generations to history; if False, don’t keep history.

• history_dir –

  directory for saving optimization history, optional.

  If the path is relative, then save relative to default_data_dir. If absolute – then save directly by specified path. If None – do not save the history to disk and keep it only in-memory.

num_of_generations: Optional[int] = None

timeout: Optional[datetime.timedelta] = datetime.timedelta(seconds=300)

early_stopping_iterations: Optional[int] = 50

early_stopping_timeout: Optional[float] = 5

keep_n_best: int = 1

max_graph_fit_time: Optional[datetime.timedelta] = None

n_jobs: int = 1

show_progress: bool = True

collect_intermediate_metric: bool = False

parallelization_mode: str = 'populational'

static_individual_metadata: dict

keep_history: bool = True

history_dir: Optional[str]

class golem.core.optimisers.optimization_parameters.GraphRequirements(num_of_generations: Optional[int] = None, timeout: Optional[datetime.timedelta] = datetime.timedelta(seconds=300), early_stopping_iterations: Optional[int] = 50, early_stopping_timeout: Optional[float] = 5, keep_n_best: int = 1, max_graph_fit_time: Optional[datetime.timedelta] = None, n_jobs: int = 1, show_progress: bool = True, collect_intermediate_metric: bool = False, parallelization_mode: str = 'populational', static_individual_metadata: dict = <factory>, keep_history: bool = True, history_dir: Optional[str] = <factory>, start_depth: int = 3, max_depth: int = 10, min_arity: int = 2, max_arity: int = 4)

Bases: golem.core.optimisers.optimization_parameters.OptimizationParameters

Defines restrictions and requirements on final graphs.

Restrictions on final graphs:

Parameters

• start_depth – start value of adaptive tree depth

• max_depth – max depth of the resulting graph

• min_arity – min number of parents for node

• max_arity – max number of parents for node

start_depth: int = 3

max_depth: int = 10

min_arity: int = 2

max_arity: int = 4

class golem.core.optimisers.optimizer.GraphGenerationParams(adapter: Optional[golem.core.adapter.adapter.BaseOptimizationAdapter] = None, rules_for_constraint: Sequence[Callable[[...], bool]] = (<function has_root>, <function has_no_cycle>, <function has_no_isolated_components>, <function has_no_self_cycled_nodes>, <function has_no_isolated_nodes>), advisor: Optional[golem.core.optimisers.advisor.DefaultChangeAdvisor] = None, node_factory: Optional[golem.core.optimisers.opt_node_factory.OptNodeFactory] = None, random_graph_factory: Optional[Callable[[golem.core.optimisers.optimization_parameters.GraphRequirements, int], golem.core.dag.graph.Graph]] = None, available_node_types: Optional[Sequence[Any]] = None, remote_evaluator: Optional[golem.core.optimisers.genetic.evaluation.DelegateEvaluator] = None)

Bases: object

This dataclass is for defining the parameters using in graph generation process

Parameters

• adapter – instance of domain graph adapter for adaptation between domain and optimization graphs

• rules_for_constraint – collection of constraints for graph verification

• advisor – instance providing task and context-specific advices for graph changes

• node_factory – instance for generating new nodes in the process of graph search

• remote_evaluator – instance of delegate evaluator for evaluation of graphs

adapter: golem.core.adapter.adapter.BaseOptimizationAdapter

verifier: golem.core.dag.graph_verifier.GraphVerifier

advisor: golem.core.optimisers.advisor.DefaultChangeAdvisor

remote_evaluator: Optional[golem.core.optimisers.genetic.evaluation.DelegateEvaluator] = None

node_factory: golem.core.optimisers.opt_node_factory.OptNodeFactory

random_graph_factory: Callable[[golem.core.optimisers.optimization_parameters.GraphRequirements, int], golem.core.dag.graph.Graph]