ontolearn.ea_initialization

Initialization for evolutionary algorithms.

Attributes

Property
Object

Classes

RandomInitMethod	Create a collection of name/value pairs.
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(*args, **kwds)

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

• attribute access:

>>> Color.RED
<Color.RED: 1>

• value lookup:

>>> Color(1)
<Color.RED: 1>

• name lookup:

>>> Color['RED']
<Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

GROW: Final

FULL: Final

RAMPED_HALF_HALF: Final

class ontolearn.ea_initialization.AbstractEAInitialization

Abstract base class for initialization methods for evolutionary algorithms.

__slots__ = ()

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

abstractmethod get_expression(pset: deap.gp.PrimitiveSetTyped) → Tree

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

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[Tree]

get_expression(pset: deap.gp.PrimitiveSetTyped, type_: type = None) → Tree

ontolearn.ea_initialization.Property

ontolearn.ea_initialization.Object

class ontolearn.ea_initialization.PropObjPair

property_: Property

object_: Object

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

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: AbstractKnowledgeBase

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: AbstractKnowledgeBase = None) → List[Tree]

get_expression(pset: deap.gp.PrimitiveSetTyped, ind: owlapy.owl_individual.OWLNamedIndividual = None) → Tree