ontolearn.learning_problem_generator
====================================

.. py:module:: ontolearn.learning_problem_generator

.. autoapi-nested-parse::

   Learning problem generator.



Attributes
----------

.. autoapisummary::

   ontolearn.learning_problem_generator.SearchAlgos


Classes
-------

.. autoapisummary::

   ontolearn.learning_problem_generator.LearningProblemGenerator


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

.. py:data:: SearchAlgos

.. py:class:: LearningProblemGenerator(knowledge_base: ontolearn.knowledge_base.KnowledgeBase, refinement_operator=None, num_problems=10000, num_diff_runs=100, min_num_instances=None, max_num_instances=sys.maxsize, min_length=3, max_length=5, depth=3, search_algo='strict-dfs')

   Learning problem generator.


   .. py:attribute:: kb


   .. py:attribute:: rho
      :value: None



   .. py:attribute:: search_algo
      :value: 'strict-dfs'



   .. py:attribute:: min_num_instances
      :value: None



   .. py:attribute:: max_num_instances
      :value: 9223372036854775807



   .. py:attribute:: min_length
      :value: 3



   .. py:attribute:: max_length
      :value: 5



   .. py:attribute:: valid_learning_problems
      :value: []



   .. py:attribute:: depth
      :value: 3



   .. py:attribute:: num_diff_runs
      :value: 100



   .. py:attribute:: num_problems
      :value: 100



   .. py:method:: export_concepts(concepts: List[ontolearn.search.Node], path: str)

      Serialise the given concepts to a file.

      :param concepts: Node objects.
      :type concepts: list
      :param path: Filename base (extension will be added automatically).
      :type path: str



   .. py:method:: concept_individuals_to_string_balanced_examples(concept: owlapy.class_expression.OWLClassExpression) -> Dict[str, Set]


   .. py:method:: get_balanced_n_samples_per_examples(*, n=5, min_num_problems=None, max_length=None, min_length=None, num_diff_runs=None, min_num_instances=None, search_algo='strict-dfs') -> Iterable[Tuple[ontolearn.search.RL_State, Set[owlapy.owl_individual.OWLNamedIndividual], Set[owlapy.owl_individual.OWLNamedIndividual]]]

      1. We generate min_num_problems number of concepts.
      2. For each concept, we generate n number of positive and negative examples.
      3. Each example contains n samples.



   .. py:method:: balanced_n_sampled_lp(n: int, string_all_pos: set)


   .. py:method:: get_balanced_examples(*, min_num_problems=None, max_length=None, min_length=None, num_diff_runs=None, min_num_instances=None, search_algo='strict-dfs') -> list

      (1) Generate valid examples with input search algorithm.
      (2) Balance valid examples.

      :returns: A list of balanced tuples (s,p,n) where s denotes the string representation of a concept, p and n denote
                a set of URIs of individuals indicating positive and negative examples.



   .. py:method:: get_examples(*, num_problems=None, max_length=None, min_length=None, num_diff_runs=None, min_num_ind=None, search_algo=None) -> list

      (1) Get valid examples with input search algorithm.

      :returns: A list of tuples (s,p,n) where s denotes the string representation of a concept, p and n denote
                a set of URIs of individuals indicating positive and negative examples.



   .. py:method:: get_concepts(*, num_problems=None, max_length=None, min_length=None, max_num_instances=None, num_diff_runs=None, min_num_instances=None, search_algo=None) -> Generator

      Calls `generate_examples`



   .. py:method:: generate_examples(*, num_problems=None, max_length=None, min_length=None, num_diff_runs=None, max_num_instances=None, min_num_instances=None, search_algo=None) -> Generator

      Generate examples via search algorithm that are valid examples w.r.t. given constraints.

      :returns: Valid examples



   .. py:method:: apply_rho_on_rl_state(rl_state)