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.

Notes

This QoI was formerly defined as just QoI.

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.

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.