dataeval.outputs.SufficiencyOutput¶

class dataeval.outputs.SufficiencyOutput¶

Output class for Sufficiency workflow.

steps¶

Array of sample sizes

Type:¶

NDArray

measures¶

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

Type:¶

Dict[str, NDArray]

n_iter¶

Number of iterations to perform in the basin-hopping curve-fit process

Type:¶

int, default 1000

inv_project(targets, n_iter=None)¶

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.

n_iter : int or None, default None¶

Iteration to use when calculating the inverse power curve, if None defaults to 1000

Returns:¶

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

Return type:¶

dict[str, NDArray]

meta()¶

Metadata about the execution of the function or method for the Output class.

Return type:¶

ExecutionMetadata

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

Notes

This method requires matplotlib to be installed.

project(projection)¶

Projects the measures for each step.

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