ontolearn.learners.clip
=======================

.. py:module:: ontolearn.learners.clip

.. autoapi-nested-parse::

   CLIP: Concept Learner with Integrated Length Prediction.



Classes
-------

.. autoapisummary::

   ontolearn.learners.clip.CLIP


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

.. py:class:: CLIP(knowledge_base: ontolearn.abstracts.AbstractKnowledgeBase, reasoner: Optional[owlapy.abstracts.AbstractOWLReasoner] = None, refinement_operator: Optional[ontolearn.abstracts.BaseRefinement[ontolearn.search.OENode]] = ExpressRefinement, quality_func: Optional[ontolearn.abstracts.AbstractScorer] = None, heuristic_func: Optional[ontolearn.abstracts.AbstractHeuristic] = None, terminate_on_goal: Optional[bool] = None, iter_bound: Optional[int] = None, max_num_of_concepts_tested: Optional[int] = None, max_runtime: Optional[int] = None, max_results: int = 10, best_only: bool = False, calculate_min_max: bool = True, path_of_embeddings='', predictor_name=None, pretrained_predictor_name=['SetTransformer', 'LSTM', 'GRU', 'CNN'], load_pretrained=False, num_workers=4, num_examples=1000, output_size=15)

   Bases: :py:obj:`ontolearn.learners.CELOE`


   Concept Learner with Integrated Length Prediction.
   This algorithm extends the CELOE algorithm by using concept length predictors and a different refinement operator,
   i.e., ExpressRefinement

   .. attribute:: best_descriptions

      Best hypotheses ordered.

      :type: EvaluatedDescriptionSet[OENode, QualityOrderedNode]

   .. attribute:: best_only

      If False pick only nodes with quality < 1.0, else pick without quality restrictions.

      :type: bool

   .. attribute:: calculate_min_max

      Calculate minimum and maximum horizontal expansion? Statistical purpose only.

      :type: bool

   .. attribute:: heuristic_func

      Function to guide the search heuristic.

      :type: AbstractHeuristic

   .. attribute:: heuristic_queue

      A sorted set that compares the nodes based on Heuristic.

      :type: SortedSet[OENode]

   .. attribute:: iter_bound

      Limit to stop the algorithm after n refinement steps are done.

      :type: int

   .. attribute:: kb

      The knowledge base that the concept learner is using.

      :type: AbstractKnowledgeBase

   .. attribute:: max_child_length

      Limit the length of concepts generated by the refinement operator.

      :type: int

   .. attribute:: max_he

      Maximal value of horizontal expansion.

      :type: int

   .. attribute:: max_num_of_concepts_tested

      

      :type: int

   .. attribute:: max_runtime

      Limit to stop the algorithm after n seconds.

      :type: int

   .. attribute:: min_he

      Minimal value of horizontal expansion.

      :type: int

   .. attribute:: name

      Name of the model = 'celoe_python'.

      :type: str

   .. attribute:: _number_of_tested_concepts

      Yes, you got it. This stores the number of tested concepts.

      :type: int

   .. attribute:: operator

      Operator used to generate refinements.

      :type: BaseRefinement

   .. attribute:: quality_func

      

      :type: AbstractScorer

   .. attribute:: reasoner

      The reasoner that this model is using.

      :type: AbstractOWLReasoner

   .. attribute:: search_tree

      Dict to store the TreeNode for a class expression.

      :type: Dict[OWLClassExpression, TreeNode[OENode]]

   .. attribute:: start_class

      The starting class expression for the refinement operation.

      :type: OWLClassExpression

   .. attribute:: start_time

      The time when :meth:`fit` starts the execution. Used to calculate the total time :meth:`fit`
      takes to execute.

      :type: float

   .. attribute:: terminate_on_goal

      Whether to stop the algorithm if a perfect solution is found.

      :type: bool


   .. py:attribute:: __slots__
      :value: ('best_descriptions', 'max_he', 'min_he', 'best_only', 'calculate_min_max', 'heuristic_queue',...



   .. py:attribute:: name
      :value: 'CLIP'



   .. py:attribute:: predictor_name
      :value: None



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



   .. py:attribute:: knowledge_base


   .. py:attribute:: load_pretrained
      :value: False



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



   .. py:attribute:: output_size
      :value: 15



   .. py:attribute:: num_examples
      :value: 1000



   .. py:attribute:: path_of_embeddings
      :value: ''



   .. py:attribute:: device


   .. py:attribute:: length_predictor


   .. py:method:: get_length_predictor()


   .. py:method:: refresh()


   .. py:method:: collate_batch(batch)


   .. py:method:: collate_batch_inference(batch)


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


   .. py:method:: predict_length(models, x_pos, x_neg)


   .. py:method:: fit(*args, **kwargs)

      Find hypotheses that explain pos and neg.



   .. py:method:: train(data: Iterable[List[Tuple]], epochs=300, batch_size=256, learning_rate=0.001, decay_rate=0.0, clip_value=5.0, save_model=True, storage_path=None, optimizer='Adam', record_runtime=True, example_sizes=None, shuffle_examples=False)

      Train RL agent on learning problems.

      :returns: self.