:py:mod:`ontolearn.utils`
=========================

.. py:module:: ontolearn.utils

.. autoapi-nested-parse::

   Ontolearn utils.



Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   log_config/index.rst
   oplogging/index.rst
   static_funcs/index.rst


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


Functions
~~~~~~~~~

.. autoapisummary::

   ontolearn.utils.setup_logging
   ontolearn.utils.compute_f1_score
   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



Attributes
~~~~~~~~~~

.. autoapisummary::

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


.. 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:data:: Factory

   

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

   

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

   

.. 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