langtest.transform.fairness.BaseFairness#

class BaseFairness#

Bases: ABC

Abstract base class for implementing accuracy measures.

alias_name#

A name or list of names that identify the accuracy measure.

Type:

str

transform(data

List[Sample], params: Dict) -> Union[List[MinScoreSample], List[MaxScoreSample]]: Transforms the input data into an output based on the implemented accuracy measure.

__init__()#

Methods

__init__()
async_run(sample_list, model, **kwargs)	Creates a task for the run method.
run(sample_list, categorised_data, **kwargs)	Computes the score for the given data.
transform(data, params)	Abstract method that implements the computation of the given measure.

Attributes

alias_name
supported_tasks
test_types

async classmethod async_run(sample_list: List[Sample], model: ModelAPI, **kwargs)#

Creates a task for the run method.

Parameters:

• sample_list (List[Sample]) – The input data to be transformed.

• model (ModelAPI) – The model to be used for the computation.

Returns:

The task for the run method.

Return type:

asyncio.Task

abstract async static run(sample_list: List[MinScoreSample], categorised_data, **kwargs) → List[Sample]#

Computes the score for the given data.

Parameters:

• sample_list (List[MinScoreSample]) – The input data to be transformed.

• model (ModelAPI) – The model to be used for the computation.

Returns:

The transformed samples.

Return type:

List[MinScoreSample]

abstract static transform(data: List[Sample], params: Dict) → List[MinScoreSample] | List[MaxScoreSample]#

Abstract method that implements the computation of the given measure.

Parameters:

• data (List[Sample]) – The input data to be transformed.

• params (Dict) – parameters for tests configuration

Returns:

The transformed data based on the implemented measure.

Return type:

Union[List[MinScoreSample], List[MaxScoreSample]]