ontolearn.learners.nces
=======================

.. py:module:: ontolearn.learners.nces

.. autoapi-nested-parse::

   NCES: Neural Class Expression Synthesis.



Classes
-------

.. autoapisummary::

   ontolearn.learners.nces.NCES


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

.. py:class:: NCES(knowledge_base, nces2_or_roces=False, quality_func: Optional[ontolearn.abstracts.AbstractScorer] = None, num_predictions=5, learner_names=['SetTransformer', 'LSTM', 'GRU'], path_of_embeddings=None, path_temp_embeddings=None, path_of_trained_models=None, auto_train=True, proj_dim=128, rnn_n_layers=2, drop_prob=0.1, num_heads=4, num_seeds=1, m=32, ln=False, dicee_model='DeCaL', dicee_epochs=5, dicee_lr=0.01, dicee_emb_dim=128, learning_rate=0.0001, tmax=20, eta_min=1e-05, clip_value=5.0, batch_size=256, num_workers=4, max_length=48, load_pretrained=True, sorted_examples=False, verbose: int = 0, enforce_validity: Optional[bool] = None)

   Bases: :py:obj:`ontolearn.base_nces.BaseNCES`


   Neural Class Expression Synthesis.


   .. py:attribute:: name
      :value: 'NCES'



   .. py:attribute:: knowledge_base


   .. py:attribute:: learner_names
      :value: ['SetTransformer', 'LSTM', 'GRU']



   .. py:attribute:: path_of_embeddings
      :value: None



   .. py:attribute:: path_temp_embeddings
      :value: None



   .. py:attribute:: path_of_trained_models
      :value: None



   .. py:attribute:: dicee_model
      :value: 'DeCaL'



   .. py:attribute:: dicee_emb_dim
      :value: 128



   .. py:attribute:: dicee_epochs
      :value: 5



   .. py:attribute:: dicee_lr
      :value: 0.01



   .. py:attribute:: rnn_n_layers
      :value: 2



   .. py:attribute:: sorted_examples
      :value: False



   .. py:attribute:: has_renamed_inds
      :value: False



   .. py:attribute:: enforce_validity
      :value: None



   .. py:method:: get_synthesizer(path=None)


   .. py:method:: refresh(path=None)


   .. py:method:: get_prediction(x_pos, x_neg)


   .. py:method:: fit_one(pos: Union[List[owlapy.owl_individual.OWLNamedIndividual], List[str]], neg: Union[List[owlapy.owl_individual.OWLNamedIndividual], List[str]])


   .. py:method:: fit(learning_problem: ontolearn.learning_problem.PosNegLPStandard, **kwargs)


   .. py:method:: best_hypotheses(n=1, return_node: bool = False) -> Union[owlapy.class_expression.OWLClassExpression, Iterable[owlapy.class_expression.OWLClassExpression], ontolearn.abstracts.AbstractNode, Iterable[ontolearn.abstracts.AbstractNode], None]


   .. py:method:: convert_to_list_str_from_iterable(data)


   .. py:method:: fit_from_iterable(dataset: Union[List[Tuple[str, Set[owlapy.owl_individual.OWLNamedIndividual], Set[owlapy.owl_individual.OWLNamedIndividual]]], List[Tuple[str, Set[str], Set[str]]]], shuffle_examples=False, verbose=False, **kwargs) -> List

      - Dataset is a list of tuples where the first items are strings corresponding to target concepts.

      - This function returns predictions as owl class expressions, not nodes as in fit



   .. py:method:: train(data: Iterable[List[Tuple]] = None, epochs=50, batch_size=64, max_num_lps=1000, refinement_expressivity=0.2, refs_sample_size=50, learning_rate=0.0001, tmax=20, eta_min=1e-05, clip_value=5.0, num_workers=8, save_model=True, storage_path=None, optimizer='Adam', record_runtime=True, example_sizes=None, shuffle_examples=False)