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(distances: np.ndarray, k: int, t: int) -> 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 ---------- distances : np.ndarray An array of distances of shape ``(N, num_neighbors)``, where N is the number of examples. Each row contains the distances to each example's `num_neighbors` nearest neighbors. It is assumed that each row is sorted in ascending order. k : int Number of neighbors used to compute the average distance to each example. This assumes that the second dimension of distances is k or greater, but it uses slicing to avoid indexing errors. t : int Controls transformation of distances between examples into similarity scores that lie in [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]]) >>> transform_distances_to_scores(distances, k=2, t=1) array([0.95122942, 0.83945702]) """ # Calculate average distance to k-nearest neighbors avg_knn_distances = distances[:, :k].mean(axis=1) # Map ood_features_scores to range 0-1 with 0 = most concerning ood_features_scores: np.ndarray = np.exp(-1 * avg_knn_distances * t) return ood_features_scores