ontolearn.learners.ocel
=======================

.. py:module:: ontolearn.learners.ocel


Classes
-------

.. autoapisummary::

   ontolearn.learners.ocel.OCEL


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

.. py:class:: OCEL(knowledge_base: ontolearn.abstracts.AbstractKnowledgeBase, 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.learners.celoe.CELOE`


   A limited version of CELOE.

   .. 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 = 'ocel_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: ()



   .. py:attribute:: name
      :value: 'ocel_python'



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

      Create a node for OCEL.

      :param c: The class expression of this node.
      :param parent_node: Parent node.
      :param is_root: Is this the root node?

      :returns: The node.
      :rtype: OENode