ontolearn.learners.tree_learner
===============================

.. py:module:: ontolearn.learners.tree_learner


Classes
-------

.. autoapisummary::

   ontolearn.learners.tree_learner.TDL


Functions
---------

.. autoapisummary::

   ontolearn.learners.tree_learner.explain_inference
   ontolearn.learners.tree_learner.concepts_reducer


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

.. py:function:: explain_inference(clf, X: pandas.DataFrame)

   Given a trained Decision Tree, extract the paths from root to leaf nodes for each entities
   https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#understanding-the-decision-tree-structure



.. py:function:: concepts_reducer(concepts: List[owlapy.class_expression.OWLClassExpression], reduced_cls: Callable) -> Union[owlapy.class_expression.OWLObjectUnionOf, owlapy.class_expression.OWLObjectIntersectionOf]

   Reduces a list of OWLClassExpression instances into a single instance of OWLObjectUnionOf or OWLObjectIntersectionOf


.. py:class:: TDL(knowledge_base, use_inverse: bool = False, use_data_properties: bool = False, use_nominals: bool = False, use_card_restrictions: bool = False, kwargs_classifier: dict = None, max_runtime: int = 1, grid_search_over: dict = None, grid_search_apply: bool = False, report_classification: bool = True, plot_tree: bool = False, plot_embeddings: bool = False, plot_feature_importance: bool = False, verbose: int = 10, verbalize: bool = False)

   Tree-based Description Logic Concept Learner


   .. py:attribute:: use_nominals
      :value: False



   .. py:attribute:: use_card_restrictions
      :value: False



   .. py:attribute:: grid_search_over
      :value: None



   .. py:attribute:: knowledge_base


   .. py:attribute:: report_classification
      :value: True



   .. py:attribute:: plot_tree
      :value: False



   .. py:attribute:: plot_embeddings
      :value: False



   .. py:attribute:: plot_feature_importance
      :value: False



   .. py:attribute:: clf
      :value: None



   .. py:attribute:: kwargs_classifier


   .. py:attribute:: max_runtime
      :value: 1



   .. py:attribute:: features
      :value: None



   .. py:attribute:: disjunction_of_conjunctive_concepts
      :value: None



   .. py:attribute:: conjunctive_concepts
      :value: None



   .. py:attribute:: owl_class_expressions


   .. py:attribute:: cbd_mapping
      :type:  Dict[str, Set[Tuple[str, str]]]


   .. py:attribute:: types_of_individuals


   .. py:attribute:: verbose
      :value: 10



   .. py:attribute:: verbalize
      :value: False



   .. py:attribute:: data_property_cast


   .. py:attribute:: X
      :value: None



   .. py:attribute:: y
      :value: None



   .. py:method:: extract_expressions_from_owl_individuals(individuals: List[owlapy.owl_individual.OWLNamedIndividual]) -> Tuple[Dict[str, owlapy.class_expression.OWLClassExpression], Dict[str, str]]


   .. py:method:: construct_sparse_binary_representations(features: List[owlapy.class_expression.OWLClassExpression], examples: List[owlapy.owl_individual.OWLNamedIndividual], examples_to_features) -> numpy.array


   .. py:method:: create_training_data(learning_problem: ontolearn.learning_problem.PosNegLPStandard) -> Tuple[pandas.DataFrame, pandas.DataFrame]


   .. py:method:: construct_owl_expression_from_tree(X: pandas.DataFrame, y: pandas.DataFrame) -> List[owlapy.class_expression.OWLObjectIntersectionOf]

      Construct an OWL class expression from a decision tree



   .. py:method:: fit(learning_problem: ontolearn.learning_problem.PosNegLPStandard = None, max_runtime: int = None)

      Fit the learner to the given learning problem

      (1) Extract multi-hop information about E^+ and E^-.
      (2) Create OWL Class Expressions from (1)
      (3) Build a binary sparse training data X where
          first |E+| rows denote the binary representations of positives
          Remaining rows denote the binary representations of E⁻
      (4) Create binary labels.
      (4) Construct a set of DL concept for each e \in E^+
      (5) Union (4)

      :param learning_problem: The learning problem
      :param max_runtime:total runtime of the learning




   .. py:property:: classification_report
      :type: str



   .. py:method:: best_hypotheses(n=1) -> Tuple[owlapy.class_expression.OWLClassExpression, List[owlapy.class_expression.OWLClassExpression]]

      Return the prediction



   .. py:method:: predict(X: List[owlapy.owl_individual.OWLNamedIndividual], proba=True) -> numpy.ndarray
      :abstractmethod:


      Predict the likelihoods of individuals belonging to the classes