ontolearn.learners.celoe
========================

.. py:module:: ontolearn.learners.celoe


Classes
-------

.. autoapisummary::

   ontolearn.learners.celoe.CELOE


Module Contents
---------------

.. py:class:: CELOE(knowledge_base: ontolearn.abstracts.AbstractKnowledgeBase = None, reasoner: Optional[owlapy.abstracts.AbstractOWLReasoner] = None, refinement_operator: Optional[ontolearn.abstracts.BaseRefinement[ontolearn.search.OENode]] = None, quality_func: Optional[ontolearn.abstracts.AbstractScorer] = None, heuristic_func: Optional[ontolearn.abstracts.AbstractHeuristic] = None, terminate_on_goal: Optional[bool] = None, iter_bound: Optional[int] = None, max_num_of_concepts_tested: Optional[int] = None, max_runtime: Optional[int] = None, max_results: int = 10, best_only: bool = False, calculate_min_max: bool = True)

   Bases: :py:obj:`ontolearn.base_concept_learner.RefinementBasedConceptLearner`


   Class Expression Learning for Ontology Engineering.
   .. attribute:: best_descriptions

      Best hypotheses ordered.

      :type: EvaluatedDescriptionSet[OENode, QualityOrderedNode]

   .. attribute:: best_only

      If False pick only nodes with quality < 1.0, else pick without quality restrictions.

      :type: bool

   .. attribute:: calculate_min_max

      Calculate minimum and maximum horizontal expansion? Statistical purpose only.

      :type: bool

   .. attribute:: heuristic_func

      Function to guide the search heuristic.

      :type: AbstractHeuristic

   .. attribute:: heuristic_queue

      A sorted set that compares the nodes based on Heuristic.

      :type: SortedSet[OENode]

   .. attribute:: iter_bound

      Limit to stop the algorithm after n refinement steps are done.

      :type: int

   .. attribute:: kb

      The knowledge base that the concept learner is using.

      :type: AbstractKnowledgeBase

   .. attribute:: max_child_length

      Limit the length of concepts generated by the refinement operator.

      :type: int

   .. attribute:: max_he

      Maximal value of horizontal expansion.

      :type: int

   .. attribute:: max_num_of_concepts_tested

      

      :type: int

   .. attribute:: max_runtime

      Limit to stop the algorithm after n seconds.

      :type: int

   .. attribute:: min_he

      Minimal value of horizontal expansion.

      :type: int

   .. attribute:: name

      Name of the model = 'celoe_python'.

      :type: str

   .. attribute:: _number_of_tested_concepts

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

      :type: int

   .. attribute:: operator

      Operator used to generate refinements.

      :type: BaseRefinement

   .. attribute:: quality_func

      

      :type: AbstractScorer

   .. attribute:: reasoner

      The reasoner that this model is using.

      :type: AbstractOWLReasoner

   .. attribute:: search_tree

      Dict to store the TreeNode for a class expression.

      :type: Dict[OWLClassExpression, TreeNode[OENode]]

   .. attribute:: start_class

      The starting class expression for the refinement operation.

      :type: OWLClassExpression

   .. attribute:: start_time

      The time when :meth:`fit` starts the execution. Used to calculate the total time :meth:`fit`
      takes to execute.

      :type: float

   .. attribute:: terminate_on_goal

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

      :type: bool


   .. py:attribute:: __slots__
      :value: ('best_descriptions', 'max_he', 'min_he', 'best_only', 'calculate_min_max', 'heuristic_queue',...



   .. py:attribute:: name
      :value: 'celoe_python'



   .. py:attribute:: search_tree
      :type:  Dict[owlapy.class_expression.OWLClassExpression, ontolearn.search.TreeNode[ontolearn.search.OENode]]


   .. py:attribute:: heuristic_queue


   .. py:attribute:: best_descriptions


   .. py:attribute:: best_only
      :value: False



   .. py:attribute:: calculate_min_max
      :value: True



   .. py:attribute:: max_he
      :value: 0



   .. py:attribute:: min_he
      :value: 1



   .. py:method:: next_node_to_expand(step: int) -> ontolearn.search.OENode

      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.
      :rtype: _N



   .. py:method:: best_hypotheses(n: int = 1, return_node: bool = False) -> Union[owlapy.class_expression.OWLClassExpression | Iterable[owlapy.class_expression.OWLClassExpression], ontolearn.search.OENode | Iterable[ontolearn.search.OENode]]

      Get the current best found hypotheses according to the quality.

      :param n: Maximum number of results.

      :returns: Iterable with hypotheses in form of search tree nodes.



   .. py:method:: make_node(c: owlapy.class_expression.OWLClassExpression, parent_node: Optional[ontolearn.search.OENode] = None, is_root: bool = False) -> ontolearn.search.OENode


   .. py:method:: updating_node(node: ontolearn.search.OENode)

      Removes the node from the heuristic sorted set and inserts it again.

      :param Node to update.:

      :Yields: The node itself.



   .. py:method:: downward_refinement(node: ontolearn.search.OENode) -> Iterable[ontolearn.search.OENode]

      Execute one refinement step of a refinement based learning algorithm.

      :param node: the search tree node on which to refine.
      :type node: _N

      :returns: Refinement results as new search tree nodes (they still need to be added to the tree).
      :rtype: Iterable[_N]



   .. py:method:: fit(*args, **kwargs)

      Find hypotheses that explain pos and neg.



   .. py:method:: encoded_learning_problem() -> Optional[ontolearn.abstracts.EncodedPosNegLPStandardKind]

      Fetch the most recently used learning problem from the fit method.



   .. py:method:: tree_node(node: ontolearn.search.OENode) -> ontolearn.search.TreeNode[ontolearn.search.OENode]

      Get the TreeNode of the given node.

      :param node: The node.

      :returns: TreeNode of the given node.



   .. py:method:: show_search_tree(heading_step: str, top_n: int = 10) -> None

      Show search tree.



   .. py:method:: update_min_max_horiz_exp(node: ontolearn.search.OENode)


   .. py:method:: clean()

      Clear all states of the concept learner.