ImageSegmentPerformance#

class ImageSegmentPerformance[source]#

Segment the data by various properties of the image, and compare the performance of the segments.

Parameters
scorers: Union[Dict[str, Union[Metric, Callable, str]], List[Any]], default: None

Scorers to override the default scorers (metrics), find more about the supported formats at https://docs.deepchecks.com/stable/user-guide/general/metrics_guide.html

image_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:

  • 'numeric' - for continuous ordinal outputs.

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

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

alternative_metricsDict[str, Metric], default: None

Deprecated, please use scorers instead.

number_of_bins: int, default5

Maximum number of bins to segment a single property into.

number_of_samples_to_infer_binsint, default1000

Minimum number of samples to use to infer the bounds of the segments’ bins

n_show_topint , default: 3

number of properties to show (shows by top diffrence by first metric)

__init__(scorers: Optional[Union[Dict[str, Union[Metric, Callable, str]], List[Any]]] = None, image_properties: Optional[List[Dict[str, Any]]] = None, alternative_metrics: Optional[Dict[str, Metric]] = None, number_of_bins: int = 5, number_of_samples_to_infer_bins: int = 1000, n_to_show: int = 3, **kwargs)[source]#
__new__(*args, **kwargs)#

Methods

ImageSegmentPerformance.add_condition(name, ...)

Add new condition function to the check.

ImageSegmentPerformance.add_condition_score_from_mean_ratio_greater_than([ratio])

Calculate for each property & metric the mean score and compares ratio between the lowest segment score and the mean score.

ImageSegmentPerformance.clean_conditions()

Remove all conditions from this check instance.

ImageSegmentPerformance.compute(context, ...)

Calculate segment performance based on image properties.

ImageSegmentPerformance.conditions_decision(result)

Run conditions on given result.

ImageSegmentPerformance.config([include_version])

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

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

Return check object from a CheckConfig object.

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

Deserialize check instance from JSON string.

ImageSegmentPerformance.initialize_run(...)

Initialize run before starting updating on batches.

ImageSegmentPerformance.metadata([with_doc_link])

Return check metadata.

ImageSegmentPerformance.name()

Name of class in split camel case.

ImageSegmentPerformance.params([show_defaults])

Return parameters to show when printing the check.

ImageSegmentPerformance.remove_condition(index)

Remove given condition by index.

ImageSegmentPerformance.run(dataset[, ...])

Run check.

ImageSegmentPerformance.to_json([indent])

Serialize check instance to JSON string.

ImageSegmentPerformance.update(context, ...)

Update the bins by the image properties.

Examples#