Calculates fairness metrics for a model over a specified dataset.
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. |