:py:mod:`ontolearn.heuristics`
==============================

.. py:module:: ontolearn.heuristics

.. autoapi-nested-parse::

   Heuristic functions.



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

Classes
~~~~~~~

.. autoapisummary::

   ontolearn.heuristics.CELOEHeuristic
   ontolearn.heuristics.DLFOILHeuristic
   ontolearn.heuristics.OCELHeuristic
   ontolearn.heuristics.CeloeBasedReward




.. py:class:: CELOEHeuristic(*, gainBonusFactor: float = 0.3, startNodeBonus: float = 0.1, nodeRefinementPenalty: float = 0.001, expansionPenaltyFactor: float = 0.1)


   Bases: :py:obj:`ontolearn.abstracts.AbstractHeuristic`\ [\ :py:obj:`ontolearn.abstracts.AbstractOEHeuristicNode`\ ]

   Heuristic like the CELOE Heuristic in DL-Learner.

   .. py:attribute:: __slots__
      :value: ('gainBonusFactor', 'startNodeBonus', 'nodeRefinementPenalty', 'expansionPenaltyFactor')

      

   .. py:attribute:: name
      :type: Final
      :value: 'CELOE_Heuristic'

      

   .. py:attribute:: gainBonusFactor
      :type: Final[float]

      

   .. py:attribute:: startNodeBonus
      :type: Final[float]

      

   .. py:attribute:: nodeRefinementPenalty
      :type: Final[float]

      

   .. py:attribute:: expansionPenaltyFactor
      :type: Final[float]

      

   .. py:method:: apply(node: ontolearn.abstracts.AbstractOEHeuristicNode, instances, learning_problem: ontolearn.abstracts.EncodedLearningProblem)

      Apply the heuristic on a search tree node and set its heuristic property to the calculated value.

      :param node: Node to set the heuristic on.
      :param instances: Set of instances covered by this node.
      :type instances: set, optional
      :param learning_problem: Underlying learning problem to compare the heuristic to.



.. py:class:: DLFOILHeuristic


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

   DLFOIL Heuristic.

   .. py:attribute:: __slots__
      :value: ()

      

   .. py:attribute:: name
      :type: Final
      :value: 'custom_dl_foil'

      

   .. py:method:: apply(node, instances, learning_problem: ontolearn.learning_problem.EncodedPosNegUndLP)

      Apply the heuristic on a search tree node and set its heuristic property to the calculated value.

      :param node: Node to set the heuristic on.
      :param instances: Set of instances covered by this node.
      :type instances: set, optional
      :param learning_problem: Underlying learning problem to compare the heuristic to.



.. py:class:: OCELHeuristic(*, gainBonusFactor: float = 0.5, expansionPenaltyFactor: float = 0.02)


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

   OCEL Heuristic.

   .. py:attribute:: __slots__
      :value: ('accuracy_method', 'gainBonusFactor', 'expansionPenaltyFactor')

      

   .. py:attribute:: name
      :type: Final
      :value: 'OCEL_Heuristic'

      

   .. py:method:: apply(node: ontolearn.search.LBLNode, instances, learning_problem: ontolearn.learning_problem.EncodedPosNegLPStandard)

      Apply the heuristic on a search tree node and set its heuristic property to the calculated value.

      :param node: Node to set the heuristic on.
      :param instances: Set of instances covered by this node.
      :type instances: set, optional
      :param learning_problem: Underlying learning problem to compare the heuristic to.



.. py:class:: CeloeBasedReward(reward_of_goal=5.0, beta=0.04, alpha=0.5)


   Reward function for DRILL.

   .. py:property:: learning_problem


   .. py:method:: apply(rl_state: ontolearn.search.RL_State, next_rl_state: ontolearn.search.RL_State)