ontolearn.utils

Ontolearn utils.

Submodules

Attributes

`Factory`
`DEFAULT_FMT`
`flag_for_performance`

Functions

`setup_logging`([config_file])	Setup logging.
`compute_f1_score`(→ float)	Compute F1-score of a concept
`f1_set_similarity`(→ float)	Compute F1 score for two set
`concept_reducer`(concepts, opt)	Reduces a set of concepts by applying a binary operation to each pair of concepts.
`concept_reducer_properties`(...)	Map a set of owl concepts and a set of properties into OWL Restrictions
`jaccard_similarity`(→ float)	Compute Jaccard Similarity
`parametrized_performance_debugger`([fmt])
`performance_debugger`(func_name)
`create_experiment_folder`([folder_name])
`serializer`(*, object_, path, serialized_name)
`deserializer`(*, path, serialized_name)
`apply_TSNE_on_df`(→ None)
`balanced_sets`(→ Tuple[Set, Set])	Balance given two sets through sampling without replacement.
`read_csv`(→ Union[None, pandas.DataFrame])	Path leads a folder containing embeddings in csv format.
`assertion_path_isfile`(→ bool)
`sanity_checking_args`(args)
`read_individuals_file`(...)	Read a text file containing IRIs of Named Individuals (one per line) and return the content as a set of OWL
`read_named_classes_file`(...)	Read a text file containing IRIs of OWL Named Classes (one per line) and return the content as a set of OWL

Package Contents

ontolearn.utils.setup_logging(config_file='ontolearn/logging.conf')[source]

Setup logging.

Parameters:: config_file (str) – Filepath for logs.

ontolearn.utils.compute_f1_score(individuals, pos, neg) → float[source]: Compute F1-score of a concept

ontolearn.utils.f1_set_similarity(y: Set[str], yhat: Set[str]) → float[source]: Compute F1 score for two set :param y: A set of URIs :param yhat: A set of URIs :return:

ontolearn.utils.concept_reducer(concepts, opt)[source]

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

Parameters:

concepts (set) – A set of concepts to be reduced.
opt (function) – A binary function that takes a pair of concepts and returns a single concept.

Returns:

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

Return type:

set

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.

ontolearn.utils.concept_reducer_properties(concepts: Set, properties, cls: Callable = None, cardinality: int = 2) → Set[owlapy.class_expression.OWLQuantifiedObjectRestriction | owlapy.class_expression.OWLObjectCardinalityRestriction][source]

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

Parameters:

concepts
properties
cls (Callable) – An owl Restriction class
cardinality – A positive Integer

Returns: List of OWL Restrictions

ontolearn.utils.Factory

ontolearn.utils.DEFAULT_FMT = 'Func:{name} took {elapsed:0.8f}s'

ontolearn.utils.flag_for_performance = False

ontolearn.utils.jaccard_similarity(y: Set[str], yhat: Set[str]) → float[source]: Compute Jaccard Similarity :param y: A set of URIs :param yhat: A set of URIs :return:

ontolearn.utils.parametrized_performance_debugger(fmt=DEFAULT_FMT)[source]

ontolearn.utils.performance_debugger(func_name)[source]

ontolearn.utils.create_experiment_folder(folder_name='Log')[source]

ontolearn.utils.serializer(*, object_: object, path: str, serialized_name: str)[source]

ontolearn.utils.deserializer(*, path: str, serialized_name: str)[source]

ontolearn.utils.apply_TSNE_on_df(df) → None[source]

ontolearn.utils.balanced_sets(a: set, b: set) → Tuple[Set, Set][source]: Balance given two sets through sampling without replacement. Returned sets have the same length. @param a: @param b: @return:

ontolearn.utils.read_csv(path) → None | pandas.DataFrame[source]: Path leads a folder containing embeddings in csv format. indexes correspond subjects or predicates or objects in n-triple. @param path: @return:

ontolearn.utils.assertion_path_isfile(path) → bool[source]

ontolearn.utils.sanity_checking_args(args)[source]

ontolearn.utils.read_individuals_file(file: str) → Set[owlapy.owl_individual.OWLNamedIndividual][source]

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

Parameters:: file – path to the text file with the IRIs of the named individuals
Returns:: set of OWLNamedIndividual with these IRIs

ontolearn.utils.read_named_classes_file(file: str) → Set[owlapy.class_expression.OWLClass][source]

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

Parameters:: file – path to the text file with the IRIs of the classes
Returns:: set of OWLNamedIndividual with these IRIs