Source code for cleanlab.internal.outlier

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"""
Helper functions used internally for outlier detection tasks.
"""

import numpy as np


[docs]def transform_distances_to_scores( avg_distances: np.ndarray, t: int, scaling_factor: float ) -> np.ndarray: """Returns an outlier score for each example based on its average distance to its k nearest neighbors. The transformation of a distance, :math:`d` , to a score, :math:`o` , is based on the following formula: .. math:: o = \\exp\\left(-dt\\right) where :math:`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]. 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]) """ # Map ood_features_scores to range 0-1 with 0 = most concerning ood_features_scores: np.ndarray = np.exp(-1 * avg_distances / scaling_factor * t) return ood_features_scores