ontolearn.learners.alcsat
=========================

.. py:module:: ontolearn.learners.alcsat

.. autoapi-nested-parse::

   ALCSAT Learner - SAT-based ALC concept learning.



Classes
-------

.. autoapisummary::

   ontolearn.learners.alcsat.ALCSAT


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

.. py:class:: ALCSAT(knowledge_base: ontolearn.abstracts.AbstractKnowledgeBase, reasoner: Optional[owlapy.abstracts.AbstractOWLReasoner] = None, max_runtime: Optional[int] = 60, max_concept_size: int = 10, start_concept_size: int = 1, operators: Optional[Set] = None, tree_templates: bool = True, type_encoding: bool = True)

   Bases: :py:obj:`ontolearn.learners.sat_base.SATBaseLearner`


   ALCSAT: SAT-based ALC concept learner.

   This learner uses SAT solvers to find ALC concept expressions that fit positive and negative examples.
   It encodes the concept learning problem as a SAT problem and uses a Glucose SAT solver to find solutions.

   The algorithm incrementally searches for concepts of increasing size (tree depth k) that maximize
   the accuracy on the given examples.

   .. attribute:: kb

      The knowledge base that the concept learner is using.

      :type: AbstractKnowledgeBase

   .. attribute:: max_concept_size

      Maximum size (depth) of concepts to search for.

      :type: int

   .. attribute:: start_concept_size

      Starting size for incremental search.

      :type: int

   .. attribute:: operators

      Set of ALC operators to use (NEG, AND, OR, EX, ALL).

      :type: Set

   .. attribute:: tree_templates

      Whether to use tree templates for symmetry breaking.

      :type: bool

   .. attribute:: type_encoding

      Whether to use type encoding optimization.

      :type: bool

   .. attribute:: timeout

      Timeout in seconds for the SAT solver (-1 for no timeout).

      :type: float

   .. attribute:: _best_hypothesis

      Best found hypothesis.

      :type: OWLClassExpression

   .. attribute:: _best_hypothesis_accuracy

      Accuracy of the best hypothesis.

      :type: float

   .. attribute:: _structure

      Internal structure representation of the knowledge base.

      :type: Structure

   .. attribute:: _ind_to_owl

      Mapping from internal individual indices to OWL individuals.

      :type: dict

   .. attribute:: _owl_to_ind

      Mapping from OWL individuals to internal indices.

      :type: dict


   .. py:attribute:: __slots__
      :value: ('max_concept_size', 'start_concept_size', 'operators', 'tree_templates', 'type_encoding')



   .. py:attribute:: name
      :value: 'alcsat'



   .. py:attribute:: max_concept_size
      :value: 10



   .. py:attribute:: start_concept_size
      :value: 1



   .. py:attribute:: operators
      :value: None



   .. py:attribute:: tree_templates
      :value: True



   .. py:attribute:: type_encoding
      :value: True



   .. py:method:: fit(lp: ontolearn.learning_problem.PosNegLPStandard)

      Find ALC concept expressions that explain positive and negative examples.

      :param lp: Learning problem with positive and negative examples.

      :returns: self