# outlier#

Helper functions used internally for outlier detection tasks.

Functions:

 transform_distances_to_scores(avg_distances, ...) Returns an outlier score for each example based on its average distance to its k nearest neighbors.
cleanlab.internal.outlier.transform_distances_to_scores(avg_distances, t, scaling_factor)[source]#

Returns an outlier score for each example based on its average distance to its k nearest neighbors.

The transformation of a distance, $d$ , to a score, $o$ , is based on the following formula:

$o = \exp\left(-dt\right)$

where $t$ scales the distance to a score in the range [0,1].

Parameters:
• avg_distances (np.ndarray) – An array of distances of shape (N), where N is the number of examples. Each entry represents an example’s average distance to its k nearest neighbors.

• t (int) – A sensitivity parameter that modulates the strength of the transformation from distances to scores. Higher values of t result in more pronounced differentiation between the scores of examples lying in the range [0,1].

• scaling_factor (float) – A scaling factor used to normalize the distances before they are converted into scores. A valid scaling factor is any positive number. The choice of scaling factor should be based on the distribution of distances between neighboring examples. A good rule of thumb is to set the scaling factor to the median distance between neighboring examples. A lower scaling factor results in more pronounced differentiation between the scores of examples lying in the range [0,1].

Return type:

ndarray

Returns:

ood_features_scores (np.ndarray) – An array of outlier scores of shape (N,) for N examples.

Examples

>>> import numpy as np
>>> from cleanlab.outlier import transform_distances_to_scores
>>> distances = np.array([[0.0, 0.1, 0.25],
...                       [0.15, 0.2, 0.3]])
>>> avg_distances = np.mean(distances, axis=1)
>>> transform_distances_to_scores(avg_distances, t=1, scaling_factor=1)
array([0.88988177, 0.80519832])