langtest.transform.fairness.MinGenderF1Score#

class MinGenderF1Score#

Bases: BaseFairness

Subclass of BaseFairness that implements the minimum F1 score.

alias_name#

The name “min_gender_f1_score” identifying the minimum F1 score.

Type:

str

transform(test

str, data: List[Sample], params: Dict) -> List[MinScoreSample]: Transforms the input data into an output based on the minimum F1 score.

__init__()#

Methods

__init__()
async_run(sample_list, model, **kwargs)	Creates a task for the run method.
run(sample_list, grouped_label, **kwargs)	Computes the minimum F1 score for the given data.
transform(test, data, params)	Computes the minimum F1 score for the given data.

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

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

Computes the minimum F1 score for the given data.

Parameters:

• sample_list (List[MinScoreSample]) – The input data samples.

• grouped_label – A dictionary containing grouped labels where each key corresponds to a test case and the value is a tuple containing true labels and predicted labels.

• **kwargs – Additional keyword arguments.

Returns:

The evaluated data samples.

Return type:

List[MinScoreSample]

classmethod transform(test: str, data: List[Sample], params: Dict) → List[MinScoreSample]#

Computes the minimum F1 score for the given data.

Parameters:

• test (str) – The test alias name.

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

• params (Dict) – Parameters for tests configuration.

Returns:

The transformed data based on the minimum F1 score.

Return type:

List[MinScoreSample]