alibi.explainers.similarity.metrics

Functions

asym_dot

asym_dot(X: numpy.ndarray, Y: numpy.ndarray, eps: float = 1e-07) -> Union[float, numpy.ndarray]

Computes the influence of training instances Y to test instances X. This is an asymmetric kernel. (:math:X^T Y/\|Y\|^2). See the paper <https://arxiv.org/abs/2102.05262>_ for more details. Each of X and Y should have a leading batch dimension of size at least 1.

Name	Type	Default	Description
X	numpy.ndarray		Matrix of vectors.
Y	numpy.ndarray		Matrix of vectors.
eps	float	1e-07	Numerical stability.

Returns

• Type: Union[float, numpy.ndarray]

cos

cos(X: numpy.ndarray, Y: numpy.ndarray, eps: float = 1e-07) -> Union[float, numpy.ndarray]

Computes the cosine between the vector(s) in X and vector Y. (:math:X^T Y/\|X\|\|Y\|). Each of X and Y should have a leading batch dimension of size at least 1.

Name	Type	Default	Description
X	numpy.ndarray		Matrix of vectors.
Y	numpy.ndarray		Matrix of vectors.
eps	float	1e-07	Numerical stability.

Returns

• Type: Union[float, numpy.ndarray]

dot

dot(X: numpy.ndarray, Y: numpy.ndarray) -> Union[float, numpy.ndarray]

Performs a dot product between the vector(s) in X and vector Y. (:math:X^T Y = \sum_i X_i Y_i). Each of X and Y should have a leading batch dimension of size at least 1.

Name	Type	Default	Description
X	numpy.ndarray		Matrix of vectors.
Y	numpy.ndarray		Matrix of vectors.

Returns

• Type: Union[float, numpy.ndarray]