ontolearn.executor
==================

.. py:module:: ontolearn.executor

.. autoapi-nested-parse::

   Model adapters.



Attributes
----------

.. autoapisummary::

   ontolearn.executor.logger
   ontolearn.executor.metrics
   ontolearn.executor.models
   ontolearn.executor.heuristics


Classes
-------

.. autoapisummary::

   ontolearn.executor.Trainer


Functions
---------

.. autoapisummary::

   ontolearn.executor.transform_string
   ontolearn.executor.compute_quality
   ontolearn.executor.execute


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

.. py:data:: logger

.. py:data:: metrics

.. py:data:: models

.. py:data:: heuristics

.. py:function:: transform_string(input_string)

   Used to turn camelCase arguments to snake_case


.. py:function:: compute_quality(KB, solution, pos, neg, qulaity_func='f1')

.. py:class:: Trainer(learner: ontolearn.base_concept_learner.BaseConceptLearner, reasoner: owlapy.abstracts.AbstractOWLReasoner)

   .. py:attribute:: learner


   .. py:attribute:: reasoner


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

      Run the concept learning algorithm according to its configuration.

      Once finished, the results can be queried with the `best_hypotheses` function.



   .. py:method:: best_hypotheses(n)

      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:: predict(individuals: List[owlapy.owl_individual.OWLNamedIndividual], hypotheses: Optional[List[Union[_N, owlapy.class_expression.OWLClassExpression]]] = None, axioms: Optional[List[owlapy.owl_axiom.OWLAxiom]] = None, n: int = 10)

      Creates a binary data frame showing for each individual whether it is entailed in the given hypotheses
              (class expressions). The individuals do not have to be in the ontology/knowledge base yet. In that case,
              axioms describing these individuals must be provided.

      The state of the knowledge base/ontology is not changed, any provided axioms will be removed again.

      :param individuals: A list of individuals/instances.
      :param hypotheses: (Optional) A list of search tree nodes or class expressions. If not provided, the
                         current :func:`BaseConceptLearner.best_hypothesis` of the concept learner are used.
      :param axioms: (Optional) A list of axioms that are not in the current knowledge base/ontology.
                     If the individual list contains individuals that are not in the ontology yet, axioms
                     describing these individuals must be provided. The argument can also be used to add
                     arbitrary axioms to the ontology for the prediction.
      :param n: Integer denoting number of ALC concepts to extract from search tree if hypotheses=None.

      :returns: Pandas data frame with dimensions |individuals|*|hypotheses| indicating for each individual and each
                hypothesis whether the individual is entailed in the hypothesis.



   .. py:method:: save_best_hypothesis(n: int = 10, path: str = 'Predictions', rdf_format: str = 'rdfxml') -> None

      Serialise the best hypotheses to a file.

      :param n: Maximum number of hypotheses to save.
      :param path: Filename base (extension will be added automatically).
      :param rdf_format: Serialisation format. currently supported: "rdfxml".



.. py:function:: execute(args)