NewLabels

class NewLabels

Detects labels that appear only in the test set.

Parameters

max_images_to_display_per_labelint , default: 3

maximum number of images to show from each newly found label in the test set.

max_new_labels_to_displayint , default: 3

Maximum number of new labels to display in output.

n_samplesOptional[int] , default10000

Number of samples to use for the check. If None, all samples will be used.

__init__(max_images_to_display_per_label: int = 3, max_new_labels_to_display: int = 3, n_samples: Optional[int] = 10000, **kwargs)

__new__(*args, **kwargs)

Methods

NewLabels.add_condition(name, ...)	Add new condition function to the check.
NewLabels.add_condition_new_label_ratio_less_or_equal([...])	Add condition - Ratio of labels that appear only in the test set required to be less or equal to the threshold.
NewLabels.clean_conditions()	Remove all conditions from this check instance.
NewLabels.compute(context)	Calculate which class_id are only available in the test data set and display them.
NewLabels.conditions_decision(result)	Run conditions on given result.
NewLabels.config([include_version, ...])	Return check configuration (conditions' configuration not yet supported).
NewLabels.from_config(conf[, version_unmatch])	Return check object from a CheckConfig object.
NewLabels.from_json(conf[, version_unmatch])	Deserialize check instance from JSON string.
NewLabels.greater_is_better()	Return True if the check reduce_output is better when it is greater.
NewLabels.initialize_run(context)	Initialize run before starting updating on batches.
NewLabels.metadata([with_doc_link])	Return check metadata.
NewLabels.name()	Name of class in split camel case.
NewLabels.params([show_defaults])	Return parameters to show when printing the check.
NewLabels.reduce_output(check_result)	Reduce check result value.
NewLabels.remove_condition(index)	Remove given condition by index.
NewLabels.run(train_dataset, test_dataset[, ...])	Run check.
NewLabels.to_json([indent, include_version, ...])	Serialize check instance to JSON string.
NewLabels.update(context, batch, dataset_kind)	No additional caching required for this check.

Examples

New Labels

previous

ImagePropertyDrift.update

next

NewLabels.add_condition