SingleDatasetPerformance

class SingleDatasetPerformance

Summarize given model performance on a dataset based on selected scorers.

Parameters

scorersUnion[List[str], Dict[str, Union[str, Callable]]], default: None

List of scorers to use. If None, use default scorers. Scorers can be supplied as a list of scorer names or as a dictionary of names and functions.

n_samplesint , default: 10_000

Maximum number of samples to use for this check.

__init__(scorers: Optional[Union[List[str], Dict[str, Union[str, Callable]]]] = None, n_samples: int = 10000, **kwargs)

__new__(*args, **kwargs)

Methods

SingleDatasetPerformance.add_condition(name, ...)	Add new condition function to the check.
SingleDatasetPerformance.add_condition_greater_than(...)	Add condition - the selected metrics scores are greater than the threshold.
SingleDatasetPerformance.clean_conditions()	Remove all conditions from this check instance.
SingleDatasetPerformance.conditions_decision(result)	Run conditions on given result.
SingleDatasetPerformance.config([...])	Return check configuration.
SingleDatasetPerformance.from_config(conf[, ...])	Return check object from a CheckConfig object.
SingleDatasetPerformance.from_json(conf[, ...])	Deserialize check instance from JSON string.
SingleDatasetPerformance.greater_is_better()	Return True if the check reduce_output is better when it is greater.
SingleDatasetPerformance.metadata([...])	Return check metadata.
SingleDatasetPerformance.name()	Name of class in split camel case.
SingleDatasetPerformance.params([show_defaults])	Return parameters to show when printing the check.
SingleDatasetPerformance.reduce_output(...)	Return the values of the metrics for the dataset provided in a {metric: value} format.
SingleDatasetPerformance.remove_condition(index)	Remove given condition by index.
SingleDatasetPerformance.run(dataset[, ...])	Run check.
SingleDatasetPerformance.run_logic(context, ...)	Run check.
SingleDatasetPerformance.to_json([indent, ...])	Serialize check instance to JSON string.

previous

model_evaluation

next

SingleDatasetPerformance.add_condition