ontolearn.learners.evolearner

EvoLearner: An evolutionary approach to learn concepts in ALCQ(D).

Classes

EvoLearner

An evolutionary approach to learn concepts in ALCQ(D).

Module Contents

class ontolearn.learners.evolearner.EvoLearner(knowledge_base: AbstractKnowledgeBase, reasoner: owlapy.abstracts.AbstractOWLReasoner | None = None, quality_func: AbstractScorer | None = None, fitness_func: AbstractFitness | None = None, init_method: AbstractEAInitialization | None = None, algorithm: AbstractEvolutionaryAlgorithm | None = None, mut_uniform_gen: AbstractEAInitialization | None = None, value_splitter: AbstractValueSplitter | None = None, terminate_on_goal: bool | None = None, max_runtime: int | None = None, use_data_properties: bool = True, use_card_restrictions: bool = True, use_inverse: bool = False, tournament_size: int = 7, card_limit: int = 10, population_size: int = 800, num_generations: int = 200, height_limit: int = 17)[source]

Bases: ontolearn.learners.base.BaseConceptLearner

An evolutionary approach to learn concepts in ALCQ(D).

algorithm

The evolutionary algorithm.

Type:: AbstractEvolutionaryAlgorithm

card_limit

The upper cardinality limit if using cardinality restriction on object properties.

Type:: int

fitness_func

Fitness function.

Type:: AbstractFitness

height_limit

The maximum value allowed for the height of the Crossover and Mutation operations.

Type:: int

init_method

The evolutionary algorithm initialization method.

Type:: AbstractEAInitialization

kb

The knowledge base that the concept learner is using.

Type:: AbstractKnowledgeBase

max_num_of_concepts_tested

Limit to stop the algorithm after n concepts tested.

Type:: int

max_runtime

max_runtime: Limit to stop the algorithm after n seconds.

Type:: int

mut_uniform_gen

The initialization method to create the tree for mutation operation.

Type:: AbstractEAInitialization

name

Name of the model = ‘evolearner’.

Type:: str

num_generations

Number of generation for the evolutionary algorithm.

Type:: int

_number_of_tested_concepts

Yes, you got it. This stores the number of tested concepts.

Type:: int

population_size

Population size for the evolutionary algorithm.

Type:: int

pset

Contains the primitives that can be used to solve a Strongly Typed GP problem.

Type:: gp.PrimitiveSetTyped

quality_func: Function to evaluate the quality of solution concepts.

reasoner

The reasoner that this model is using.

Type:: AbstractOWLReasoner

start_time

The time when fit() starts the execution. Used to calculate the total time fit() takes to execute.

Type:: float

terminate_on_goal

Whether to stop the algorithm if a perfect solution is found.

Type:: bool

toolbox

A toolbox for evolution that contains the evolutionary operators.

Type:: base.Toolbox

tournament_size

The number of evolutionary individuals participating in each tournament.

Type:: int

use_card_restrictions

Use cardinality restriction for object properties?

Type:: bool

use_data_properties

Consider data properties?

Type:: bool

use_inverse

Consider inversed concepts?

Type:: bool

value_splitter

Used to calculate the splits for data properties values.

Type:: AbstractValueSplitter

__slots__ = ('fitness_func', 'init_method', 'algorithm', 'value_splitter', 'tournament_size',...

name = 'evolearner'

kb: AbstractKnowledgeBase

fitness_func: AbstractFitness

init_method: AbstractEAInitialization

algorithm: AbstractEvolutionaryAlgorithm

mut_uniform_gen: AbstractEAInitialization

value_splitter: AbstractValueSplitter

use_data_properties: bool

use_card_restrictions: bool

use_inverse: bool

tournament_size: int

card_limit: int

population_size: int

num_generations: int

height_limit: int

generator: ConceptGenerator

pset: deap.gp.PrimitiveSetTyped

toolbox: deap.base.Toolbox

reasoner = None

total_fits = 0

register_op(alias: str, function: Callable, *args, **kargs)[source]

Register a function in the toolbox under the name alias. You may provide default arguments that will be passed automatically when calling the registered function. Fixed arguments can then be overriden at function call time.

Parameters:

alias – The name the operator will take in the toolbox. If the alias already exist it will overwrite the operator already present.
function – The function to which refer the alias.
args – One or more argument (and keyword argument) to pass automatically to the registered function when called, optional.

fit(*args, **kwargs) → EvoLearner[source]: Find hypotheses that explain pos and neg.

best_hypotheses(n: int = 1, key: str = 'fitness', return_node: bool = False) → owlapy.class_expression.OWLClassExpression | Iterable[owlapy.class_expression.OWLClassExpression][source]

Get the current best found hypotheses according to the quality.

Parameters:: n – Maximum number of results.
Returns:: Iterable with hypotheses in form of search tree nodes.

clean(partial: bool = False)[source]: Clear all states of the concept learner.