:py:mod:`ontolearn.metrics`
===========================

.. py:module:: ontolearn.metrics

.. autoapi-nested-parse::

   Quality metrics for concept learners.



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

Classes
~~~~~~~

.. autoapisummary::

   ontolearn.metrics.Recall
   ontolearn.metrics.Precision
   ontolearn.metrics.F1
   ontolearn.metrics.Accuracy
   ontolearn.metrics.WeightedAccuracy




.. py:class:: Recall(*args, **kwargs)


   Bases: :py:obj:`ontolearn.abstracts.AbstractScorer`

   Recall quality function.

   Attribute:
       name: name of the metric = 'Recall'.

   .. py:attribute:: __slots__
      :value: ()

      

   .. py:attribute:: name
      :type: Final
      :value: 'Recall'

      

   .. py:method:: score2(tp: int, fn: int, fp: int, tn: int) -> Tuple[bool, float]

      Quality score for a coverage count.

      :param tp: True positive count.
      :param fn: False negative count.
      :param fp: False positive count.
      :param tn: True negative count.

      :returns:

                Tuple, first position indicating if the function could be applied, second position the quality value
                   in the range 0.0--1.0.



.. py:class:: Precision(*args, **kwargs)


   Bases: :py:obj:`ontolearn.abstracts.AbstractScorer`

   Precision quality function.

   Attribute:
       name: name of the metric = 'Precision'.

   .. py:attribute:: __slots__
      :value: ()

      

   .. py:attribute:: name
      :type: Final
      :value: 'Precision'

      

   .. py:method:: score2(tp: int, fn: int, fp: int, tn: int) -> Tuple[bool, float]

      Quality score for a coverage count.

      :param tp: True positive count.
      :param fn: False negative count.
      :param fp: False positive count.
      :param tn: True negative count.

      :returns:

                Tuple, first position indicating if the function could be applied, second position the quality value
                   in the range 0.0--1.0.



.. py:class:: F1(*args, **kwargs)


   Bases: :py:obj:`ontolearn.abstracts.AbstractScorer`

   F1-score quality function.

   Attribute:
       name: name of the metric = 'F1'.

   .. py:attribute:: __slots__
      :value: ()

      

   .. py:attribute:: name
      :type: Final
      :value: 'F1'

      

   .. py:method:: score2(tp: int, fn: int, fp: int, tn: int) -> Tuple[bool, float]

      Quality score for a coverage count.

      :param tp: True positive count.
      :param fn: False negative count.
      :param fp: False positive count.
      :param tn: True negative count.

      :returns:

                Tuple, first position indicating if the function could be applied, second position the quality value
                   in the range 0.0--1.0.



.. py:class:: Accuracy(*args, **kwargs)


   Bases: :py:obj:`ontolearn.abstracts.AbstractScorer`

   Accuracy quality function.
   Accuracy is acc = (tp + tn) / (tp + tn + fp+ fn).
   However, Concept learning papers (e.g. Learning OWL Class expression) appear to invent their own accuracy metrics.

   In OCEL =>    Accuracy of a concept = 1 - ( \|E^+ \ R(C)\|+ \|E^- AND R(C)\|) / \|E\|).


   In CELOE  =>    Accuracy of a concept C = 1 - ( \|R(A) \ R(C)\| + \|R(C) \ R(A)\|)/n.



   1) R(.) is the retrieval function, A is the class to describe and C in CELOE.

   2) E^+ and E^- are the positive and negative examples probided. E = E^+ OR E^- .

   Attribute:
       name: name of the metric = 'Accuracy'.

   .. py:attribute:: __slots__
      :value: ()

      

   .. py:attribute:: name
      :type: Final
      :value: 'Accuracy'

      

   .. py:method:: score2(tp: int, fn: int, fp: int, tn: int) -> Tuple[bool, float]

      Quality score for a coverage count.

      :param tp: True positive count.
      :param fn: False negative count.
      :param fp: False positive count.
      :param tn: True negative count.

      :returns:

                Tuple, first position indicating if the function could be applied, second position the quality value
                   in the range 0.0--1.0.



.. py:class:: WeightedAccuracy(*args, **kwargs)


   Bases: :py:obj:`ontolearn.abstracts.AbstractScorer`

   WeightedAccuracy quality function.

   Attribute:
       name: name of the metric = 'WeightedAccuracy'.

   .. py:attribute:: __slots__
      :value: ()

      

   .. py:attribute:: name
      :type: Final
      :value: 'WeightedAccuracy'

      

   .. py:method:: score2(tp: int, fn: int, fp: int, tn: int) -> Tuple[bool, float]

      Quality score for a coverage count.

      :param tp: True positive count.
      :param fn: False negative count.
      :param fp: False positive count.
      :param tn: True negative count.

      :returns:

                Tuple, first position indicating if the function could be applied, second position the quality value
                   in the range 0.0--1.0.