ontolearn.learners.celoe
Classes
Class Expression Learning for Ontology Engineering. |
Module Contents
- class ontolearn.learners.celoe.CELOE(knowledge_base: KnowledgeBase = None, reasoner: owlapy.abstracts.AbstractOWLReasoner | None = None, refinement_operator: BaseRefinement[OENode] | None = None, quality_func: AbstractScorer | None = None, heuristic_func: AbstractHeuristic | None = None, terminate_on_goal: bool | None = None, iter_bound: int | None = None, max_num_of_concepts_tested: int | None = None, max_runtime: int | None = None, max_results: int = 10, best_only: bool = False, calculate_min_max: bool = True)[source]
Bases:
ontolearn.base_concept_learner.RefinementBasedConceptLearnerClass Expression Learning for Ontology Engineering. .. attribute:: best_descriptions
Best hypotheses ordered.
- type:
EvaluatedDescriptionSet[OENode, QualityOrderedNode]
- best_only
If False pick only nodes with quality < 1.0, else pick without quality restrictions.
- Type:
bool
- calculate_min_max
Calculate minimum and maximum horizontal expansion? Statistical purpose only.
- Type:
bool
- heuristic_func
Function to guide the search heuristic.
- Type:
- iter_bound
Limit to stop the algorithm after n refinement steps are done.
- Type:
int
- kb
The knowledge base that the concept learner is using.
- Type:
- max_child_length
Limit the length of concepts generated by the refinement operator.
- Type:
int
- max_he
Maximal value of horizontal expansion.
- Type:
int
- max_num_of_concepts_tested
- Type:
int
- max_runtime
Limit to stop the algorithm after n seconds.
- Type:
int
- min_he
Minimal value of horizontal expansion.
- Type:
int
- name
Name of the model = ‘celoe_python’.
- Type:
str
- _number_of_tested_concepts
Yes, you got it. This stores the number of tested concepts.
- Type:
int
- operator
Operator used to generate refinements.
- Type:
- quality_func
- Type:
- reasoner
The reasoner that this model is using.
- Type:
AbstractOWLReasoner
- search_tree
Dict to store the TreeNode for a class expression.
- start_class
The starting class expression for the refinement operation.
- Type:
OWLClassExpression
- start_time
The time when
fit()starts the execution. Used to calculate the total timefit()takes to execute.- Type:
float
- terminate_on_goal
Whether to stop the algorithm if a perfect solution is found.
- Type:
bool
- __slots__ = ('best_descriptions', 'max_he', 'min_he', 'best_only', 'calculate_min_max', 'heuristic_queue',...
- name = 'celoe_python'
- heuristic_queue
- best_descriptions
- best_only
- calculate_min_max
- max_he = 0
- min_he = 1
- next_node_to_expand(step: int) OENode[source]
Return from the search tree the most promising search tree node to use for the next refinement step.
- Returns:
Next search tree node to refine.
- Return type:
_N
- best_hypotheses(n: int = 1, return_node: bool = False) owlapy.class_expression.OWLClassExpression | Iterable[owlapy.class_expression.OWLClassExpression] | OENode | Iterable[OENode][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.
- make_node(c: owlapy.class_expression.OWLClassExpression, parent_node: OENode | None = None, is_root: bool = False) OENode[source]
- updating_node(node: OENode)[source]
Removes the node from the heuristic sorted set and inserts it again.
- Parameters:
update. (Node to)
- Yields:
The node itself.
- downward_refinement(node: OENode) Iterable[OENode][source]
Execute one refinement step of a refinement based learning algorithm.
- Parameters:
node (_N) – the search tree node on which to refine.
- Returns:
Refinement results as new search tree nodes (they still need to be added to the tree).
- Return type:
Iterable[_N]
- encoded_learning_problem() EncodedPosNegLPStandardKind | None[source]
Fetch the most recently used learning problem from the fit method.