:py:mod:`ontolearn.learners.tree_learner`
=========================================

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


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

Classes
~~~~~~~

.. autoapisummary::

   ontolearn.learners.tree_learner.TDL



Functions
~~~~~~~~~

.. autoapisummary::

   ontolearn.learners.tree_learner.is_float
   ontolearn.learners.tree_learner.compute_quality
   ontolearn.learners.tree_learner.make_iterable_verbose
   ontolearn.learners.tree_learner.extract_cbd
   ontolearn.learners.tree_learner.explain_inference
   ontolearn.learners.tree_learner.concepts_reducer



.. py:function:: is_float(value)


.. py:function:: compute_quality(instances, pos, neg, conf_matrix=False, quality_func=None)


.. py:function:: make_iterable_verbose(iterable_object, verbose, desc='Default') -> Iterable


.. py:function:: extract_cbd(dataframe) -> Dict[str, List[Tuple[str, str]]]

   Extract concise bounded description for each entity, where the entity is a subject entity.
   Create a mapping from a node to out-going edges and connected nodes
   :param dataframe:
   :return:


.. py:function:: explain_inference(clf, X_test: 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, quality_func: Callable = None, kwargs_classifier: dict = None, max_runtime: int = 1, grid_search_over: dict = None, grid_search_apply: bool = False, report_classification: bool = False, plot_tree: bool = False, plot_embeddings: bool = False, verbose: int = 1)


   Tree-based Description Logic Concept Learner

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

      Create a training data (X:pandas.DataFrame of (n,d) , y:pandas.Series of (n,1)) for binary class problem.
      n denotes the number of examples
      d denotes the number of features extracted from n examples.

      return X, y


   .. 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^- denoted by \mathcal{F}.
      (1.1) E = list of (E^+ \sqcup E^-).
      (2) Build a training data \mathbf{X} \in  \mathbb{R}^{ |E|      imes |\mathcal{F}| } .
      (3) Create binary labels \mathbf{X}.

      (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: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