ontolearn.utils
===============

.. py:module:: ontolearn.utils

.. autoapi-nested-parse::

   Ontolearn utils.



Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/ontolearn/utils/log_config/index
   /autoapi/ontolearn/utils/oplogging/index
   /autoapi/ontolearn/utils/static_funcs/index


Attributes
----------

.. autoapisummary::

   ontolearn.utils.Factory
   ontolearn.utils.DEFAULT_FMT
   ontolearn.utils.flag_for_performance


Functions
---------

.. autoapisummary::

   ontolearn.utils.setup_logging
   ontolearn.utils.compute_f1_score
   ontolearn.utils.f1_set_similarity
   ontolearn.utils.concept_reducer
   ontolearn.utils.concept_reducer_properties
   ontolearn.utils.jaccard_similarity
   ontolearn.utils.parametrized_performance_debugger
   ontolearn.utils.performance_debugger
   ontolearn.utils.create_experiment_folder
   ontolearn.utils.serializer
   ontolearn.utils.deserializer
   ontolearn.utils.apply_TSNE_on_df
   ontolearn.utils.balanced_sets
   ontolearn.utils.read_csv
   ontolearn.utils.assertion_path_isfile
   ontolearn.utils.sanity_checking_args
   ontolearn.utils.read_individuals_file
   ontolearn.utils.read_named_classes_file


Package Contents
----------------

.. py:function:: setup_logging(config_file='ontolearn/logging.conf')

   Setup logging.

   :param config_file: Filepath for logs.
   :type config_file: str


.. py:function:: compute_f1_score(individuals, pos, neg) -> float

   Compute F1-score of a concept



.. py:function:: f1_set_similarity(y: Set[str], yhat: Set[str]) -> float

   Compute F1 score for two set
   :param y: A set of URIs
   :param yhat: A set of URIs
   :return:


.. py:function:: concept_reducer(concepts, opt)

   Reduces a set of concepts by applying a binary operation to each pair of concepts.

   :param concepts: A set of concepts to be reduced.
   :type concepts: set
   :param opt: A binary function that takes a pair of concepts and returns a single concept.
   :type opt: function

   :returns: A set containing the results of applying the binary operation to each pair of concepts.
   :rtype: set

   .. rubric:: Example

   >>> concepts = {1, 2, 3}
   >>> opt = lambda x: x[0] + x[1]
   >>> concept_reducer(concepts, opt)
   {2, 3, 4, 5, 6}

   .. note:: The operation `opt` should be commutative and associative to ensure meaningful reduction in the context of set operations.


.. py:function:: concept_reducer_properties(concepts: Set, properties, cls: Callable = None, cardinality: int = 2) -> Set[Union[owlapy.class_expression.OWLQuantifiedObjectRestriction, owlapy.class_expression.OWLObjectCardinalityRestriction]]

   Map a set of owl concepts and a set of properties into OWL Restrictions

   :param concepts:
   :param properties:
   :param cls: An owl Restriction class
   :type cls: Callable
   :param cardinality: A positive Integer

   Returns: List of OWL Restrictions



.. py:data:: Factory

.. py:data:: DEFAULT_FMT
   :value: 'Func:{name} took {elapsed:0.8f}s'


.. py:data:: flag_for_performance
   :value: False


.. py:function:: jaccard_similarity(y: Set[str], yhat: Set[str]) -> float

   Compute Jaccard Similarity
   :param y: A set of URIs
   :param yhat: A set of URIs
   :return:


.. py:function:: parametrized_performance_debugger(fmt=DEFAULT_FMT)

.. py:function:: performance_debugger(func_name)

.. py:function:: create_experiment_folder(folder_name='Log')

.. py:function:: serializer(*, object_: object, path: str, serialized_name: str)

.. py:function:: deserializer(*, path: str, serialized_name: str)

.. py:function:: apply_TSNE_on_df(df) -> None

.. py:function:: balanced_sets(a: set, b: set) -> Tuple[Set, Set]

   Balance given two sets through sampling without replacement.
   Returned sets have the same length.
   @param a:
   @param b:
   @return:


.. py:function:: read_csv(path) -> Union[None, pandas.DataFrame]

   Path leads a folder containing embeddings in csv format.
   indexes correspond subjects or predicates or objects in n-triple.
   @param path:
   @return:


.. py:function:: assertion_path_isfile(path) -> bool

.. py:function:: sanity_checking_args(args)

.. py:function:: read_individuals_file(file: str) -> Set[owlapy.owl_individual.OWLNamedIndividual]

   Read a text file containing IRIs of Named Individuals (one per line) and return the content as a set of OWL
   Named Individuals

   :param file: path to the text file with the IRIs of the named individuals

   :returns: set of OWLNamedIndividual with these IRIs


.. py:function:: read_named_classes_file(file: str) -> Set[owlapy.class_expression.OWLClass]

   Read a text file containing IRIs of OWL Named Classes (one per line) and return the content as a set of OWL
   Classes

   :param file: path to the text file with the IRIs of the classes

   :returns: set of OWLNamedIndividual with these IRIs