sharp.qoi.DiffQoI

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

A general QoI, suitable for models/methods that output label predictions or scores. target_function can output either scores or binary labels.

Parameters:
target_functionfunction

Method used to predict a label or score. The output of this function should be a 1-dimensional array with the expected target (i.e., label or score) for each of the passed observations.

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 just QoI.


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.