fatf.transparency.models.submodular_pick
.submodular_pick¶

fatf.transparency.models.submodular_pick.
submodular_pick
(dataset, explain_instance, sample_size=0, explanations_number=5)[source]¶ Applies submodular pick to explanations of a given subset of data.
New in version 0.1.1.
Chooses the most informative data point explanations using the submodular pick algorithm introduced by [RIBEIRO2016WHY]. Submodular pick applies a greedy optimisation to maximise the coverage function for explanations of a subset of points taken from the input data set. The explanation function (
explain_instance
) must have exactly one required parameter and return a dictionary mapping feature names or indices to their respective importance. Parameters
 datasetnumpy.ndarray
A data set from which to select individual instances to be explained.
 explain_instancecallable
A reference to a function or method that can generate an explanation from an array representing an individual instance. This callable must accept exactly one required parameter and return an explanation around the selected data point – a dictionary mapping feature names or indices to their importance.
 sample_sizeinteger, optional (default=0)
The number of (randomly selected) data points for which to generate explanations. If
0
, explanations for all the data points in thedataset
will be generated. explanations_numberinteger, optional (default=5)
The number of explanations to return. If
0
, an ordered list of all explanations generated for the selected data subset are returned.
 Returns
 sp_explanationsList[Dictionary[Union[integer, string], Number]]
List of explanations chosen by the submodular pick algorithm.
 sp_indicesList[integer]
List of indices for rows in the
dataset
chosen (and explained) by the submodular pick algorithm.
 Raises
 IncorrectShapeError
The input data set is not a 2dimensional numpy array.
 TypeError
sample_size
orexplanations_number
is not an integer.explain_instance
is not Python callable (function or method). ValueError
The input data set must only contain base types (strings and numbers).
sample_size
orexplanations_number
is a negative integer. The number of requested explanations is larger than the number of samples in the data set. RuntimeError
The
explain_instance
callable does not require exactly one parameter.
 Warns
 UserWarning
sample_size
is larger than the number of instances (rows) available in thedataset
, in which case the entire data set is used. The number of the requested explanations is larger than the number of instances selected to generate explanations – explanations for all the data points in the sample will be generated.
References
 RIBEIRO2016WHY
Ribeiro, M.T., Singh, S. and Guestrin, C., 2016, August. Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 11351144). ACM.