class LabelPropertyOutliers[source]#

Find outliers labels with respect to the given properties.

The check computes several label properties and then computes the number of outliers for each property. The check uses IQR to detect outliers out of the single dimension properties.

label_propertiesList[Dict[str, Any]], default: None

List of properties. Replaces the default deepchecks properties. Each property is a dictionary with keys 'name' (str), method (Callable) and 'output_type' (str), representing attributes of said method. ‘output_type’ must be one of:

  • 'numerical' - for continuous ordinal outputs.

  • 'categorical' - for discrete, non-ordinal outputs. These can still be numbers, but these numbers do not have inherent value.

  • 'class_id' - for properties that return the class_id. This is used because these properties are later matched with the VisionData.label_map, if one was given.

For more on image / label properties, see the guide about Vision Properties.

n_show_topint , default: 3

number of outliers to show from each direction (upper limit and bottom limit)

iqr_percentiles: Tuple[int, int], default: (25, 75)

Two percentiles which define the IQR range

iqr_scale: float, default: 1.5

The scale to multiply the IQR range for the outliers detection

min_samplesint , default: 10

Minimum number of samples required to calculate IQR. If there are not enough non-null samples a specific property, the check will skip it. If all properties are skipped, the check will raise a NotEnoughSamplesError.

n_samplesOptional[int] , default10000

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

__init__(label_properties: Optional[List[Dict[str, Any]]] = None, n_show_top: int = 3, iqr_percentiles: Tuple[int, int] = (25, 75), iqr_scale: float = 1.5, min_samples: int = 10, n_samples: Optional[int] = 10000, **kwargs)[source]#
__new__(*args, **kwargs)#


LabelPropertyOutliers.add_condition(name, ...)

Add new condition function to the check.


Remove all conditions from this check instance.

LabelPropertyOutliers.compute(context, ...)

Compute final result.


Run conditions on given result.


Return check configuration (conditions' configuration not yet supported).

LabelPropertyOutliers.from_config(conf[, ...])

Return check object from a CheckConfig object.

LabelPropertyOutliers.from_json(conf[, ...])

Deserialize check instance from JSON string.


Return default properties to run in the check.


Initialize the properties state.


Return check metadata.

Name of class in split camel case.


Return parameters to show when printing the check.


Remove given condition by index.[, ...])

Run check.

LabelPropertyOutliers.to_json([indent, ...])

Serialize check instance to JSON string.

LabelPropertyOutliers.update(context, batch, ...)

Aggregate image properties from batch.