dataeval.workflows.SufficiencyOutput¶

class dataeval.workflows.SufficiencyOutput¶

Output class for Sufficiency workflow.

steps¶

Array of sample sizes

Type:¶

NDArray

params¶

Inverse power curve coefficients for the line of best fit for each measure

Type:¶

Dict[str, NDArray]

measures¶

Average of values observed for each sample size step for each measure

Type:¶

Dict[str, NDArray]

dict()¶

Output attributes as a dictionary.

Return type:¶

dict[str, Any]

inv_project(targets)¶

Calculate training samples needed to achieve target model metric values.

Parameters:¶

targets : Mapping[str, ArrayLike]¶

Mapping of target metric scores (from 0.0 to 1.0) that we want to achieve, where the key is the name of the metric.

Returns:¶

List of the number of training samples needed to achieve each corresponding entry in targets

Return type:¶

dict[str, NDArray]

meta()¶

Execution metadata as a dictionary.

Return type:¶

dict[str, Any]

plot(class_names=None)¶

Plotting function for data sufficience tasks

Parameters:¶

class_names : Sequence[str] | None, default None¶

List of class names

Returns:¶

List of Figures for each measure

Return type:¶

list[Figure]

Raises:¶

ValueError – If the length of data points in the measures do not match

project(projection)¶

Projects the measures for each value of X

Parameters:¶

projection : int | Iterable[int]¶

Step or steps to project

Returns:¶

Dataclass containing the projected measures per projection

Return type:¶

SufficiencyOutput

Raises:¶

ValueError – If the length of data points in the measures do not match If projection is not numerical