:py:mod:`ontolearn.binders`
===========================

.. py:module:: ontolearn.binders

.. autoapi-nested-parse::

   Pyhon binders of other concept learners.



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

Classes
~~~~~~~

.. autoapisummary::

   ontolearn.binders.PredictedConcept
   ontolearn.binders.DLLearnerBinder




.. py:class:: PredictedConcept(**kwargs)


   .. py:method:: __iter__()



.. py:class:: DLLearnerBinder(binary_path=None, model=None, kb_path=None, storage_path='.', max_runtime=3)


   dl-learner python binder.

   .. py:method:: write_dl_learner_config(pos: List[str], neg: List[str]) -> str

      Writes config file for dl-learner.

      :param pos: A list of URIs of individuals indicating positive examples in concept learning problem.
      :param neg: A list of URIs of individuals indicating negatives examples in concept learning problem.

      :returns: Path of generated config file.
      :rtype: str


   .. py:method:: fit(lp: ontolearn.learning_problem.PosNegLPStandard, max_runtime: int = None)

      Fit dl-learner model on a given positive and negative examples.

      :param lp: PosNegLPStandard
      :param lp.pos A list of URIs of individuals indicating positive examples in concept learning problem.:
      :param lp.neg A list of URIs of individuals indicating negatives examples in concept learning problem.:
      :param max_runtime: Limit to stop the algorithm after n seconds.

      :returns: self.


   .. py:method:: best_hypotheses(n: int = None) -> PredictedConcept


   .. py:method:: best_hypothesis()

      Return predictions if exists.

      :returns: The prediction or the string 'No prediction found.'


   .. py:method:: parse_dl_learner_output(output_of_dl_learner: List[str], file_path: str) -> Dict

      Parse the output received from executing dl-learner.

      :param output_of_dl_learner: The output of dl-learner to parse.
      :param file_path: The file path to store the output.

      :returns: ..., 'Accuracy': ..., 'F-measure': ...}.
      :rtype: A dictionary of {'Prediction'


   .. py:method:: train(dataset: List = None) -> None
      :staticmethod:

      Dummy method, currently it does nothing.


   .. py:method:: fit_from_iterable(dataset: List = None, max_runtime=None) -> List[Dict]
      :abstractmethod:

      Fit dl-learner model on a list of given positive and negative examples.

      :param dataset: A list of tuple (s,p,n) where
                      s => string representation of target concept,
                      p => positive examples, i.e. s(p)=1 and
                      n => negative examples, i.e. s(n)=0.
      :param max_runtime: Limit to stop the algorithm after n seconds.

      :returns: self.