client.get_explanation

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.
input_data_source	Union[fdl.RowDataSource, fdl.EventIdDataSource]	None	Type of data source for the input dataset to compute explanation on (RowDataSource, EventIdDataSource). A single row explanation is currently supported.
ref_data_source	Optional[Union[fdl.DatasetDataSource, fdl.SqlSliceQueryDataSource] ]	None	Type of data source for the reference data to compute explanation on (DatasetDataSource, SqlSliceQueryDataSource). Only used for non-text models and the following methods: 'SHAP', 'FIDDLER_SHAP', 'PERMUTE', 'MEAN_RESET'
explanation_type	Optional[str]	'FIDDLER_SHAP'	Explanation method name. Could be your custom explanation method or one of the following method: 'SHAP', 'FIDDLER_SHAP', 'IG', 'PERMUTE', 'MEAN_RESET', 'ZERO_RESET'
num_permutations	Optional[int]	300	- For Fiddler SHAP, num_permutations corresponds to the number of coalitions to sample to estimate the Shapley values of each single-reference game. - For the permutation algorithms, num_permutations corresponds to the number of permutations from the dataset to use for the computation.
ci_level	Optional[float]	0.95	The confidence level (between 0 and 1).
top_n_class	Optional[int]	None	For multi-class classification models only, specifying if only the n top classes are computed or all classes (when parameter is None).

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

# FIDDLER SHAP - Dataset reference data source
row = df.to_dict(orient='records')[0]
client.get_explanation(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_data_source=fdl.RowDataSource(row=row),
    ref_data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID, num_samples=300),
    explanation_type='FIDDLER_SHAP',
    num_permutations=200,
    ci_level=0.95,
)

# FIDDLER SHAP - Slice ref data source
row = df.to_dict(orient='records')[0]
query = f'SELECT * from {DATASET_ID}.{MODEL_ID} WHERE sulphates >= 0.8'
client.get_explanation(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_data_source=fdl.RowDataSource(row=row),
    ref_data_source=fdl.SqlSliceQueryDataSource(query=query, num_samples=100),
    explanation_type='FIDDLER_SHAP',
    num_permutations=200,
    ci_level=0.95,
)

# FIDDLER SHAP - Multi-class classification (top classes)
row = df.to_dict(orient='records')[0]
client.get_explanation(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_data_source=fdl.RowDataSource(row=row),
    ref_data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID),
    explanation_type='FIDDLER_SHAP',
    top_n_class=2
)

# IG (Not available by default, need to be enabled via package.py)
row = df.to_dict(orient='records')[0]
client.get_explanation(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_data_source=fdl.RowDataSource(row=row),
    explanation_type='IG',
)

Return Type	Description
tuple	A named tuple with the explanation results.