ConflictingLabels

class ConflictingLabels

Find samples which have the exact same features’ values but different labels.

Parameters

columnsUnion[Hashable, List[Hashable]] , default: None

List of columns to check, if none given checks all columns Except ignored ones.

ignore_columnsUnion[Hashable, List[Hashable]] , default: None

List of columns to ignore, if none given checks based on columns variable.

n_to_showint , default: 5

number of most common ambiguous samples to show.

__init__(columns: Optional[Union[Hashable, List[Hashable]]] = None, ignore_columns: Optional[Union[Hashable, List[Hashable]]] = None, n_to_show: int = 5, **kwargs)

__new__(*args, **kwargs)

Methods

ConflictingLabels.add_condition(name, ...)	Add new condition function to the check.
ConflictingLabels.add_condition_ratio_of_conflicting_labels_less_or_equal([...])	Add condition - require ratio of samples with conflicting labels less or equal to max_ratio.
ConflictingLabels.clean_conditions()	Remove all conditions from this check instance.
ConflictingLabels.conditions_decision(result)	Run conditions on given result.
ConflictingLabels.config()	Return check configuration (conditions' configuration not yet supported).
ConflictingLabels.from_config(conf)	Return check object from a CheckConfig object.
ConflictingLabels.metadata([with_doc_link])	Return check metadata.
ConflictingLabels.name()	Name of class in split camel case.
ConflictingLabels.params([show_defaults])	Return parameters to show when printing the check.
ConflictingLabels.remove_condition(index)	Remove given condition by index.
ConflictingLabels.run(dataset[, model, ...])	Run check.
ConflictingLabels.run_logic(context, ...)	Run check.

Examples

Conflicting Labels

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DataDuplicates.run_logic

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ConflictingLabels.add_condition