outlier#
Helper functions used internally for outlier detection tasks.
Functions:

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

Ensure that scores where avg_distances are below the tolerance threshold get a score of one. 
 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])
 cleanlab.internal.outlier.correct_precision_errors(scores, avg_distances, metric, C=100, p=None)[source]#
Ensure that scores where avg_distances are below the tolerance threshold get a score of one.
 Parameters:
scores (
ndarray
) – An array of scores of shape(N)
, where N is the number of examples. Each entry represents a score between 0 and 1.avg_distances (
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.metric (
str
) – The metric used by the knn algorithm to calculate the distances. It must be ‘cosine’, ‘euclidean’ or ‘minkowski’, otherwise this function does nothing.C (
int
) – Multiplier used to increase the tolerance of the acceptable precision differences. It is a multiplicative factor of the machine epsilon that is used to calculate the tolerance. For the type of values that are used in the distances, a value of 100 should be a sensible default value for small values of the distances, below the order of 1.p (
Optional
[int
]) – This value is only used when metric is ‘minkowski’. A ValueError will be raised if metric is ‘minkowski’ and ‘p’ was not provided.
 Returns:
fixed_scores
– An array of scores of shape(N,)
for N examples with scores between 0 and 1.