ontolearn.lp_generator
======================

.. py:module:: ontolearn.lp_generator


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/ontolearn/lp_generator/generate_data/index
   /autoapi/ontolearn/lp_generator/helper_classes/index


Classes
-------

.. autoapisummary::

   ontolearn.lp_generator.LPGen
   ontolearn.lp_generator.KB2Data


Package Contents
----------------

.. py:class:: LPGen(kb_path, storage_path=None, max_num_lps=1000, beyond_alc=False, depth=3, max_child_length=20, refinement_expressivity=0.2, downsample_refinements=True, sample_fillers_count=10, num_sub_roots=50, min_num_pos_examples=1)

   .. py:attribute:: lp_gen


   .. py:method:: generate()


.. py:class:: KB2Data(path, storage_path=None, max_num_lps=1000, beyond_alc=False, depth=3, max_child_length=20, refinement_expressivity=0.2, downsample_refinements=True, sample_fillers_count=10, num_sub_roots=50, min_num_pos_examples=1)

   This class takes an owl file, loads it into a knowledge base using ontolearn.knowledge_base.KnowledgeBase.
   A refinement operator is used to generate a large number of concepts, from which we filter and retain the shortest
   non-redundant concepts. We export each concept and its instances (eventually positive and negative examples) into
   a json file.


   .. py:attribute:: path


   .. py:attribute:: max_num_lps
      :value: 1000



   .. py:attribute:: beyond_alc
      :value: False



   .. py:attribute:: dl_syntax_renderer


   .. py:attribute:: kb


   .. py:attribute:: num_examples


   .. py:attribute:: min_num_pos_examples
      :value: 1



   .. py:attribute:: atomic_concept_names


   .. py:attribute:: lp_gen


   .. py:method:: find_optimal_number_of_examples()


   .. py:method:: generate_descriptions()


   .. py:method:: sample_examples(pos, neg)


   .. py:method:: save_data()