cleanlab--cleanlab
Elías Snorrason
72 行
2.9 KiB
Python
72 行
2.9 KiB
Python
# Copyright (C) 2017-2023 Cleanlab Inc.
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# This file is part of cleanlab.
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#
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# cleanlab is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published
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# by the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# cleanlab is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
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"""
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Helper functions used internally for outlier detection tasks.
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"""
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import numpy as np
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def transform_distances_to_scores(
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avg_distances: np.ndarray, t: int, scaling_factor: float
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) -> np.ndarray:
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"""Returns an outlier score for each example based on its average distance to its k nearest neighbors.
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The transformation of a distance, :math:`d` , to a score, :math:`o` , is based on the following formula:
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.. math::
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o = \\exp\\left(-dt\\right)
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where :math:`t` scales the distance to a score in the range [0,1].
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Parameters
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----------
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avg_distances : np.ndarray
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An array of distances of shape ``(N)``, where N is the number of examples.
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Each entry represents an example's average distance to its k nearest neighbors.
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t : int
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A sensitivity parameter that modulates the strength of the transformation from distances to scores.
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Higher values of `t` result in more pronounced differentiation between the scores of examples
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lying in the range [0,1].
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scaling_factor : float
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A scaling factor used to normalize the distances before they are converted into scores. A valid
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scaling factor is any positive number. The choice of scaling factor should be based on the
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distribution of distances between neighboring examples. A good rule of thumb is to set the
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scaling factor to the median distance between neighboring examples. A lower scaling factor
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results in more pronounced differentiation between the scores of examples lying in the range [0,1].
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Returns
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-------
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ood_features_scores : np.ndarray
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An array of outlier scores of shape ``(N,)`` for N examples.
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Examples
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--------
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>>> import numpy as np
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>>> from cleanlab.outlier import transform_distances_to_scores
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>>> distances = np.array([[0.0, 0.1, 0.25],
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... [0.15, 0.2, 0.3]])
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>>> avg_distances = np.mean(distances, axis=1)
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>>> transform_distances_to_scores(avg_distances, t=1, scaling_factor=1)
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array([0.88988177, 0.80519832])
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"""
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# Map ood_features_scores to range 0-1 with 0 = most concerning
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ood_features_scores: np.ndarray = np.exp(-1 * avg_distances / scaling_factor * t)
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return ood_features_scores
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