# Source code for cleanlab.internal.outlier

# Copyright (C) 2017-2023  Cleanlab Inc.
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"""
Helper functions used internally for outlier detection tasks.
"""

from typing import Optional
import numpy as np

from cleanlab.internal.constants import EPSILON

[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
return np.exp(-t * avg_distances / max(scaling_factor, EPSILON))

[docs]def correct_precision_errors(
scores: np.ndarray,
avg_distances: np.ndarray,
metric: str,
C: int = 100,
p: Optional[int] = None,
):
"""
Ensure that scores where avg_distances are below the tolerance threshold get a score of one.

Parameters
----------
scores :
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 :
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 :
The metric used by the knn algorithm to calculate the distances.
It must be 'cosine', 'euclidean' or 'minkowski', otherwise this function does nothing.

C :
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 :
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.
"""
if metric == "cosine":
tolerance = C * np.finfo(np.float_).epsneg
elif metric == "euclidean":
tolerance = np.sqrt(C * np.finfo(np.float_).eps)
elif metric == "minkowski":
if p is None:
raise ValueError("When metric is 'minkowski' you must specify the 'p' parameter")
tolerance = (C * np.finfo(np.float_).eps) ** (1 / p)
else:
return scores