ontolearn.ea_initialization

Initialization for evolutionary algorithms.

Attributes

`Property`
`Object`

Classes

`RandomInitMethod`	Generic enumeration.
`AbstractEAInitialization`	Abstract base class for initialization methods for evolutionary algorithms.
`EARandomInitialization`	Rnndom initialization methods for evolutionary algorithms.
`PropObjPair`
`EARandomWalkInitialization`	Random walk initialization for description logic learning.

Module Contents

class ontolearn.ea_initialization.RandomInitMethod[source]

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

GROW: Final

FULL: Final

RAMPED_HALF_HALF: Final

class ontolearn.ea_initialization.AbstractEAInitialization[source]

Abstract base class for initialization methods for evolutionary algorithms.

__slots__ = ()

abstract get_population(container: Callable, pset: deap.gp.PrimitiveSetTyped, population_size: int = 0) → List[ontolearn.ea_utils.Tree][source]

abstract get_expression(pset: deap.gp.PrimitiveSetTyped) → ontolearn.ea_utils.Tree[source]

class ontolearn.ea_initialization.EARandomInitialization(min_height: int = 3, max_height: int = 6, method: RandomInitMethod = RandomInitMethod.RAMPED_HALF_HALF)[source]

Bases: AbstractEAInitialization

Rnndom initialization methods for evolutionary algorithms.

__slots__ = ('min_height', 'max_height', 'method')

min_height: int

max_height: int

method: RandomInitMethod

get_population(container: Callable, pset: deap.gp.PrimitiveSetTyped, population_size: int = 0) → List[ontolearn.ea_utils.Tree][source]

get_expression(pset: deap.gp.PrimitiveSetTyped, type_: type = None) → ontolearn.ea_utils.Tree[source]

ontolearn.ea_initialization.Property

ontolearn.ea_initialization.Object

class ontolearn.ea_initialization.PropObjPair[source]

property_: Property

object_: Object

class ontolearn.ea_initialization.EARandomWalkInitialization(max_t: int = 2, jump_pr: float = 0.5)[source]

Bases: AbstractEAInitialization

Random walk initialization for description logic learning.

__slots__ = ('max_t', 'jump_pr', 'type_counts', 'dp_to_prim_type', 'dp_splits', 'kb')

connection_pr: float = 0.5

max_t: int

jump_pr: float

type_counts: Dict[owlapy.class_expression.OWLClass, int]

dp_to_prim_type: Dict[owlapy.owl_property.OWLDataProperty, Any]

dp_splits: Dict[owlapy.owl_property.OWLDataProperty, List[owlapy.owl_literal.OWLLiteral]]

kb: KnowledgeBase

get_population(container: Callable, pset: deap.gp.PrimitiveSetTyped, population_size: int = 0, pos: List[owlapy.owl_individual.OWLNamedIndividual] = None, dp_to_prim_type: Dict[owlapy.owl_property.OWLDataProperty, Any] = None, dp_splits: Dict[owlapy.owl_property.OWLDataProperty, List[owlapy.owl_literal.OWLLiteral]] = None, kb: KnowledgeBase = None) → List[ontolearn.ea_utils.Tree][source]

get_expression(pset: deap.gp.PrimitiveSetTyped, ind: owlapy.owl_individual.OWLNamedIndividual = None) → ontolearn.ea_utils.Tree[source]