sharp.qoi.LikelihoodQoI

class sharp.qoi.LikelihoodQoI(target_function=None, X=None)[source]

Implements equation 3 from [1]. This QoI is designed for binary classification problems only. It calculates the difference between the likelihoods for rows1 and rows2 to obtain the positive label. target_function should output either scores or class label predictions.

Methods

calculate(rows1, rows2)

Calculates the influence score based on the target_function outputs for rows1 and rows2.

estimate(rows)

Prepares and runs self.target_function for a set of samples.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

Notes

This QoI was formerly defined as BCLikelihood.

References

[1]

Datta, A., Sen, S., & Zick, Y. (2016). Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In 2016 IEEE symposium on security and privacy (SP) (pp. 598-617). IEEE.


calculate(rows1, rows2)

Calculates the influence score based on the target_function outputs for rows1 and rows2.

Parameters:
rows1array-like of shape (n_samples, n_features)

First set of samples to compare.

rows2array-like of shape (n_samples, n_features)

Second set of samples to compare.

Returns:
influence_scorefloat

Influence score for rows1, compared to rows2.

estimate(rows)

Prepares and runs self.target_function for a set of samples.

Parameters:
rowsarray-like of shape (n_samples, n_features)

Samples over which target_function will be applied.

Returns:
target_outputnp.ndarray of shape (n_samples,)

Label predictions or score estimations.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.