client.get_feature

Input Parameter	Type	Default	Description
project_id	str	None	A unique identifier for the project.
model_id	str	None	A unique identifier for the model.
data_source	Union[fdl.DatasetDataSource, fdl.SqlSliceQueryDataSource]	None	Type of data source for the input dataset to compute feature importance on (DatasetDataSource or SqlSliceQueryDataSource)
num_iterations	Optional[int]	10000	The maximum number of ablated model inferences per feature.
num_refs	Optional[int]	10000	Number of reference points used in the explanation.
ci_level	Optional[float]	0.95	The confidence level (between 0 and 1).
overwrite_cache	Optional[bool]	False	Whether to overwrite the feature importance cached values or not

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


# Feature Importance - Dataset data source
feature_importance = client.get_feature_importance(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID, num_samples=200),
    num_iterations=300,
    num_refs=200,
    ci_level=0.90,
)

# Feature Importance - Slice Query data source
query = f'SELECT * FROM {DATASET_ID}.{MODEL_ID} WHERE CreditScore > 700'
feature_importance = client.get_feature_importance(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.SqlSliceQueryDataSource(query=query, num_samples=80),
    num_iterations=300,
    num_refs=200,
    ci_level=0.90,
)

Return Type	Description
tuple	A named tuple with the feature impact results.