ontolearn.nces_utils
====================

.. py:module:: ontolearn.nces_utils

.. autoapi-nested-parse::

   NCES utils.



Classes
-------

.. autoapisummary::

   ontolearn.nces_utils.SimpleSolution


Functions
---------

.. autoapisummary::

   ontolearn.nces_utils.sample_examples
   ontolearn.nces_utils.try_get_embs
   ontolearn.nces_utils.generate_training_data


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

.. py:class:: SimpleSolution(vocab, atomic_concept_names)

   .. py:attribute:: name
      :value: 'SimpleSolution'



   .. py:attribute:: atomic_concept_names


   .. py:attribute:: tokenizer


   .. py:method:: predict(expression: str)


.. py:function:: sample_examples(pos, neg, num_ex)

.. py:function:: try_get_embs(pos, neg, embeddings, num_examples)

   Depending on the KGE model, some individuals do not get assigned to any embedding during training. This function filters out such individuals from the provided positive/negative examples. It also


.. py:function:: generate_training_data(kb_path, kb, max_num_lps=1000, refinement_expressivity=0.2, refs_sample_size=50, beyond_alc=True, storage_path=None)