:py:mod:`ontolearn.lp_generator.helper_classes`
===============================================

.. py:module:: ontolearn.lp_generator.helper_classes


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

Classes
~~~~~~~

.. autoapisummary::

   ontolearn.lp_generator.helper_classes.ConceptDescriptionGenerator
   ontolearn.lp_generator.helper_classes.RDFTriples
   ontolearn.lp_generator.helper_classes.KB2Data




.. py:class:: ConceptDescriptionGenerator(knowledge_base, refinement_operator, depth=2, max_length=10, num_sub_roots=150)


   Learning problem generator.

   .. py:method:: apply_rho(concept)


   .. py:method:: generate()



.. py:class:: RDFTriples(kb_path, storage_dir=None)


   The knowledge graph/base is converted into triples of the form: individual_i ---role_j---> concept_k or
   individual_i ---role_j---> individual_k and stored in a txt file for the computation of embeddings.

   .. py:method:: export_triples(export_folder_name='triples')



.. py:class:: KB2Data(path, storage_dir=None, max_num_lps=1000, 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:method:: find_optimal_number_of_examples()


   .. py:method:: generate_descriptions()


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


   .. py:method:: save_data()