# Copyright (C) 2017-2023 Cleanlab Inc.
# This file is part of cleanlab.
# cleanlab is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# cleanlab is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
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:
o = \\exp\\left(-dt\\right)
where :math:`t` scales the distance to a score in the range [0,1].
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].
ood_features_scores : np.ndarray
An array of outlier scores of shape ``(N,)`` for N 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)
# 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)