client.run_fairness

Get fairness metrics for a model over a dataset.

Input Parameter	Type	Default	Description
project_id	str	None	The unique identifier for the project.
model_id	str	None	The unique identifier for the model.
dataset_id	str	None	The unique identifier for the dataset.
protected_features	list	None	A list of protected features.
positive_outcome	Union [str, int]	None	The name or value of the positive outcome for the model.
slice_query	Optional [str]	None	A SQL query. If specified, fairness metrics will only be calculated over the dataset slice specified by the query.
score_threshold	Optional [float]	0.5	The score threshold used to calculate model outcomes.

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'
DATASET_ID = 'example_dataset'

protected_features = [
    'feature_1',
    'feature_2'
]

positive_outcome = 1

fairness_metrics = client.run_fairness(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    dataset_id=DATASET_ID,
    protected_features=protected_features,
    positive_outcome=positive_outcome
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'
DATASET_ID = 'example_dataset'

protected_features = [
    'feature_1',
    'feature_2'
]

positive_outcome = 1

slice_query = f""" SELECT * FROM "{DATASET_ID}.{MODEL_ID}" WHERE feature_1 < 20.0 LIMIT 100 """

fairness_metrics = client.run_fairness(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    dataset_id=DATASET_ID,
    protected_features=protected_features,
    positive_outcome=positive_outcome,
    slice_query=slice_query
)

Return Type	Description
dict	A dictionary containing fairness metric results.