:py:mod:`ontolearn.search`
==========================

.. py:module:: ontolearn.search

.. autoapi-nested-parse::

   Node representation.



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

Classes
~~~~~~~

.. autoapisummary::

   ontolearn.search.Node
   ontolearn.search.OENode
   ontolearn.search.EvoLearnerNode
   ontolearn.search.NCESNode
   ontolearn.search.RL_State
   ontolearn.search.LBLNode
   ontolearn.search.LengthOrderedNode
   ontolearn.search.HeuristicOrderedNode
   ontolearn.search.QualityOrderedNode
   ontolearn.search.SearchTreePriorityQueue
   ontolearn.search.TreeNode
   ontolearn.search.DRILLSearchTreePriorityQueue
   ontolearn.search.EvaluatedConcept
   ontolearn.search.SuperProp




.. py:class:: Node(concept: owlapy.class_expression.OWLClassExpression, length: int)


   Bases: :py:obj:`_NodeConcept`, :py:obj:`_NodeLen`, :py:obj:`_NodeIndividualsCount`, :py:obj:`ontolearn.abstracts.AbstractNode`

   Simple node.


   .. py:attribute:: __slots__
      :value: ('_concept', '_len', '_individuals_count')

      

   .. py:method:: __str__()

      Return str(self).



.. py:class:: OENode(concept: owlapy.class_expression.OWLClassExpression, length: int, parent_node: Optional[OENode] = None, is_root: bool = False)


   Bases: :py:obj:`_NodeConcept`, :py:obj:`_NodeLen`, :py:obj:`_NodeIndividualsCount`, :py:obj:`_NodeQuality`, :py:obj:`_NodeHeuristic`, :py:obj:`_NodeParentRef`\ [\ :py:obj:`OENode`\ ], :py:obj:`ontolearn.abstracts.AbstractNode`, :py:obj:`ontolearn.abstracts.AbstractConceptNode`, :py:obj:`ontolearn.abstracts.AbstractOEHeuristicNode`

   OENode search tree node.

   .. py:property:: h_exp
      :type: int

      Get horizontal expansion.

      :returns: Horizontal expansion.

   .. py:property:: refinement_count
      :type: int

      Get the refinement count for this node.

      :returns: Refinement count.

   .. py:attribute:: __slots__
      :value: ('_concept', '_len', '_individuals_count', '_quality', '_heuristic', '_parent_ref',...

      

   .. py:attribute:: renderer
      :type: ClassVar[owlapy.owl_object.OWLObjectRenderer]

      

   .. py:method:: increment_h_exp()


   .. py:method:: __str__()

      Return str(self).



.. py:class:: EvoLearnerNode(concept: owlapy.class_expression.OWLClassExpression, length: int, individuals_count: int, quality: float, tree_length: int, tree_depth: int)


   Bases: :py:obj:`_NodeConcept`, :py:obj:`_NodeLen`, :py:obj:`_NodeIndividualsCount`, :py:obj:`_NodeQuality`, :py:obj:`ontolearn.abstracts.AbstractNode`, :py:obj:`ontolearn.abstracts.AbstractConceptNode`

   EvoLearner search tree node.

   .. py:property:: tree_length


   .. py:property:: tree_depth


   .. py:attribute:: __slots__
      :value: ('_concept', '_len', '_individuals_count', '_quality', '_tree_length', '_tree_depth')

      

   .. py:method:: __str__()

      Return str(self).



.. py:class:: NCESNode(concept: owlapy.class_expression.OWLClassExpression, length: int, individuals_count: int, quality: float)


   Bases: :py:obj:`_NodeConcept`, :py:obj:`_NodeLen`, :py:obj:`_NodeIndividualsCount`, :py:obj:`_NodeQuality`, :py:obj:`ontolearn.abstracts.AbstractNode`, :py:obj:`ontolearn.abstracts.AbstractConceptNode`

   EvoLearner search tree node.

   .. py:attribute:: __slots__
      :value: ('_concept', '_len', '_individuals_count', '_quality')

      

   .. py:method:: __str__()

      Return str(self).



.. py:class:: RL_State(concept: owlapy.class_expression.OWLClassExpression, parent_node: Optional[RL_State] = None, embeddings=None, is_root: bool = False, length=None)


   Bases: :py:obj:`_NodeConcept`, :py:obj:`_NodeQuality`, :py:obj:`_NodeHeuristic`, :py:obj:`ontolearn.abstracts.AbstractNode`, :py:obj:`_NodeParentRef`\ [\ :py:obj:`RL_State`\ ]

   Abstract search tree node.

   .. py:attribute:: renderer
      :type: ClassVar[owlapy.owl_object.OWLObjectRenderer]

      RL_State node.

   .. py:attribute:: __slots__
      :value: ('_concept', 'embeddings', '_quality', '_heuristic', 'length', 'parent_node', 'is_root',...

      

   .. py:method:: __str__()

      Return str(self).


   .. py:method:: __lt__(other)

      Return self<value.


   .. py:method:: __gt__(other)

      Return self>value.



.. py:class:: LBLNode(concept: owlapy.class_expression.OWLClassExpression, length: int, individuals, parent_node: Optional[LBLNode] = None, is_root: bool = False)


   Bases: :py:obj:`_NodeIndividuals`, :py:obj:`OENode`

   LBL search tree node.

   .. py:property:: children


   .. py:property:: parent_node
      :type: Optional[LBLNode]

      Get the parent node.

      :returns: Parent node.

   .. py:attribute:: __slots__
      :value: ('_children', '_individuals')

      

   .. py:method:: add_child(n)


   .. py:method:: remove_child(n)



.. py:class:: LengthOrderedNode(node: _N, length: int)


   Bases: :py:obj:`Generic`\ [\ :py:obj:`_N`\ ]

   Abstract base class for generic types.

   A generic type is typically declared by inheriting from
   this class parameterized with one or more type variables.
   For example, a generic mapping type might be defined as::

     class Mapping(Generic[KT, VT]):
         def __getitem__(self, key: KT) -> VT:
             ...
         # Etc.

   This class can then be used as follows::

     def lookup_name(mapping: Mapping[KT, VT], key: KT, default: VT) -> VT:
         try:
             return mapping[key]
         except KeyError:
             return default

   .. py:attribute:: __slots__
      :value: ('node', 'len')

      

   .. py:attribute:: node
      :type: Final[_N]

      

   .. py:attribute:: len
      :type: Final[int]

      

   .. py:method:: __lt__(other)

      Return self<value.


   .. py:method:: __eq__(other)

      Return self==value.



.. py:class:: HeuristicOrderedNode(node: _N)


   Bases: :py:obj:`Generic`\ [\ :py:obj:`_N`\ ]

   A comparator that orders the Nodes based on Heuristic, then OrderedOWLObject of the concept

   .. py:attribute:: __slots__
      :value: 'node'

      

   .. py:attribute:: node
      :type: Final[_N]

      

   .. py:method:: __lt__(other: _N)

      Return self<value.


   .. py:method:: __eq__(other: _N)

      Return self==value.



.. py:class:: QualityOrderedNode(node: OENode)


   QualityOrderedNode search tree node.

   .. py:attribute:: __slots__
      :value: 'node'

      

   .. py:attribute:: node
      :type: Final[OENode]

      

   .. py:method:: __lt__(other)

      Return self<value.


   .. py:method:: __eq__(other)

      Return self==value.



.. py:class:: SearchTreePriorityQueue(quality_func, heuristic_func)


   Bases: :py:obj:`ontolearn.abstracts.LBLSearchTree`\ [\ :py:obj:`LBLNode`\ ]

   Search tree based on priority queue.

   :param quality_func: An instance of a subclass of AbstractScorer that measures the quality of a node.
   :param heuristic_func: An instance of a subclass of AbstractScorer that measures the promise of a node.

   .. attribute:: quality_func

      An instance of a subclass of AbstractScorer that measures the quality of a node.

   .. attribute:: heuristic_func

      An instance of a subclass of AbstractScorer that measures the promise of a node.

   .. attribute:: items_in_queue

      An instance of PriorityQueue Class.

   .. attribute:: .nodes

      A dictionary where keys are string representation of nodes and values are corresponding node objects.

   .. attribute:: nodes

      A property method for ._nodes.

   .. attribute:: expressionTests

      not being used .

   .. attribute:: str_to_obj_instance_mapping

      not being used.

   .. py:attribute:: quality_func
      :type: ontolearn.abstracts.AbstractScorer

      

   .. py:attribute:: heuristic_func
      :type: ontolearn.abstracts.AbstractHeuristic

      

   .. py:attribute:: nodes
      :type: Dict[owlapy.class_expression.OWLClassExpression, LBLNode]

      

   .. py:attribute:: items_in_queue
      :type: PriorityQueue[Tuple[float, HeuristicOrderedNode[LBLNode]]]

      

   .. py:method:: add(n: LBLNode)

      Append a node into the search tree.

      :param n: A Node object

      :returns: None


   .. py:method:: add_root(node, kb_learning_problem)

      Add the root node to the search tree.

      :param node: Root node to add.
      :param kb_learning_problem: Underlying learning problem to compare the quality to.


   .. py:method:: add_node(*, node: LBLNode, parent_node: LBLNode, kb_learning_problem: ontolearn.abstracts.EncodedLearningProblem) -> Optional[bool]

      Add a node into the search tree after calculating heuristic value given its parent.

      :param node: A Node object
      :param parent_node: A Node object
      :param kb_learning_problem: the encoded learning problem to compare the quality on

      :returns: True if node is a "goal node", i.e. quality_metric(node)=1.0
                False if node is a "weak node", i.e. quality_metric(node)=0.0
                None otherwise

      .. rubric:: Notes

      node is a refinement of refined_node


   .. py:method:: get_most_promising() -> LBLNode

      Gets the current most promising node from Queue.

      :returns: A node object
      :rtype: node


   .. py:method:: get_top_n(n: int, key='quality') -> List[LBLNode]

      Gets the top n nodes determined by key from the search tree.

      :returns: A list of node objects
      :rtype: top_n_predictions


   .. py:method:: clean()

      Reset the search tree state.


   .. py:method:: show_search_tree(root_concept: owlapy.class_expression.OWLClassExpression, heading_step: str)

      Debugging function to print the search tree to standard output.

      :param root_concept: The tree is printed starting from this search tree node.
      :param heading_step: Message to print at top of the output.



.. py:class:: TreeNode(node: _N, parent_tree_node: Optional[_TN] = None, is_root: bool = False)


   Bases: :py:obj:`Generic`\ [\ :py:obj:`_N`\ ]

   Simple search tree node.

   .. py:attribute:: __slots__
      :value: ('children', 'node')

      

   .. py:attribute:: node
      :type: Final[_N]

      

   .. py:attribute:: children
      :type: Set[TreeNode[_N]]

      


.. py:class:: DRILLSearchTreePriorityQueue


   Bases: :py:obj:`ontolearn.abstracts.DRILLAbstractTree`

   #@TODO Move to learners/drill.py

   Search tree based on priority queue.

   :param quality_func:
   :type quality_func: An instance of a subclass of AbstractScorer that measures the quality of a node.
   :param heuristic_func:
   :type heuristic_func: An instance of a subclass of AbstractScorer that measures the promise of a node.

   .. attribute:: quality_func



      :type: An instance of a subclass of AbstractScorer that measures the quality of a node.

   .. attribute:: heuristic_func



      :type: An instance of a subclass of AbstractScorer that measures the promise of a node.

   .. attribute:: items_in_queue



      :type: An instance of PriorityQueue Class.

   .. attribute:: .nodes



      :type: A dictionary where keys are string representation of nodes and values are corresponding node objects.

   .. attribute:: nodes



      :type: A property method for ._nodes.

   .. attribute:: expressionTests



      :type: not being used .

   .. attribute:: str_to_obj_instance_mapping



      :type: not being used.

   .. py:method:: add(node: RL_State)

      Append a node into the search tree.
      :param node:
      :type node: A RL_State object

      :rtype: None


   .. py:method:: show_current_search_tree(top_n=10)

      Show search tree.


   .. py:method:: get_most_promising() -> RL_State

      Gets the current most promising node from Queue.

      :returns: **node**
      :rtype: A node object


   .. py:method:: get_top_n(n: int, key='quality') -> List[Node]

      Gets the top n nodes determined by key from the search tree.

      :returns: **top_n_predictions**
      :rtype: A list of node objects


   .. py:method:: clean()



.. py:class:: EvaluatedConcept


   Explicitly declare the attributes that should be returned by the evaluate_concept method of a KnowledgeBase.

   This way, Python uses a more efficient way to store the instance attributes, which can significantly reduce the
   memory usage.

   .. py:attribute:: __slots__
      :value: ('q', 'inds', 'ic')

      


.. py:class:: SuperProp(super_obj)


   Super wrapper which allows property setting & deletion. Super can't be subclassed with empty __init__ arguments.

   .. py:method:: __getattr__(name)


   .. py:method:: __setattr__(name, value)

      Implement setattr(self, name, value).


   .. py:method:: __delattr__(name)

      Implement delattr(self, name).