ontolearn.learners.nces2
========================

.. py:module:: ontolearn.learners.nces2

.. autoapi-nested-parse::

   NCES2: Neural Class Expression Synthesis in ALCHIQ(D).



Classes
-------

.. autoapisummary::

   ontolearn.learners.nces2.NCES2


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

.. py:class:: NCES2(knowledge_base, nces2_or_roces=True, quality_func: Optional[ontolearn.abstracts.AbstractScorer] = None, num_predictions=5, path_of_trained_models=None, auto_train=True, proj_dim=128, drop_prob=0.1, num_heads=4, num_seeds=1, m=[32, 64, 128], ln=False, embedding_dim=128, sampling_strategy='nces2', input_dropout=0.0, feature_map_dropout=0.1, kernel_size=4, num_of_output_channels=32, 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, verbose: int = 0, data=[], enforce_validity: Optional[bool] = None)

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


   Neural Class Expression Synthesis in ALCHIQ(D).


   .. py:attribute:: name
      :value: 'NCES2'



   .. py:attribute:: knowledge_base


   .. py:attribute:: knowledge_base_path


   .. py:attribute:: triples_data


   .. py:attribute:: num_entities


   .. py:attribute:: num_relations


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



   .. py:attribute:: embedding_dim
      :value: 128



   .. py:attribute:: sampling_strategy
      :value: 'nces2'



   .. py:attribute:: input_dropout
      :value: 0.0



   .. py:attribute:: feature_map_dropout
      :value: 0.1



   .. py:attribute:: kernel_size
      :value: 4



   .. py:attribute:: num_of_output_channels
      :value: 32



   .. py:attribute:: num_workers
      :value: 4



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



   .. py:method:: get_synthesizer(path=None, verbose=True)


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


   .. py:method:: get_prediction(dataloaders, return_normalize_scores=False)


   .. 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(data: 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

      - data 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, shuffle_examples=False)