ImageSegmentPerformance

class ImageSegmentPerformance

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

Deprecated since version 0.9.3: The ImageSegmentPerformance check is deprecated and will be removed in the 0.11 version. Please use the WeakSegmentsPerformance check instead.

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)

__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([...])	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

Image Segment Performance

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ConfusionMatrixReport.update_object_detection

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ImageSegmentPerformance.add_condition