Get fairness analysis on a dataset or a slice.
Only Binary classification models with categorical protected attributes are currently supported.
| 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. |
| data_source | Union[fdl.DatasetDataSource, fdl.SqlSliceQueryDataSource] | None | DataSource for the input dataset to compute fairness on (DatasetDataSource or SqlSliceQueryDataSource). |
| protected_features | list[str] | None | A list of protected features. |
| positive_outcome | Union[str, int, float, bool] | None | Value of the positive outcome (from the target column) for Fairness analysis. |
| 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'
# Fairness - Dataset data source
fairness_metrics = client.get_fairness(
project_id=PROJECT_ID,
model_id=MODEL_ID,
data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID, num_samples=200),
protected_features=['feature_1', 'feature_2'],
positive_outcome='Approved',
score_threshold=0.6
)
# Fairness - Slice Query data source
query = f'SELECT * FROM {DATASET_ID}.{MODEL_ID} WHERE CreditSCore > 700'
fairness_metrics = client.get_fairness(
project_id=PROJECT_ID,
model_id=MODEL_ID,
data_source=fdl.SqlSliceQueryDataSource(query=query, num_samples=200),
protected_features=['feature_1', 'feature_2'],
positive_outcome='Approved',
score_threshold=0.6
)
| Return Type | Description |
|---|---|
| dict | A dictionary containing fairness metric results. |