import numpy as np
import scipy.stats as stats
from .base import BaseThresholder
from .thresh_utility import check_scores, normalize
[docs]
class ZSCORE(BaseThresholder):
r"""ZSCORE class for ZSCORE thresholder.
Use the zscore to evaluate a non-parametric means to threshold
scores generated by the decision_scores where outliers are set
to any value beyond a zscore of one.
See :cite:`bagdonavicius2020zscore` for details.
Parameters
----------
random_state : int, optional (default=1234)
Random seed for the random number generators of the thresholders. Can also
be set to None.
Attributes
----------
thresh_ : threshold value that separates inliers from outliers
dscores_ : 1D array of decomposed decision scores
Notes
-----
The z-score can be calculated as follows:
.. math::
Z = \frac{x-\bar{x}}{\sigma} \mathrm{,}
where :math:`\bar{x}` and :math:`\sigma` are the mean and the
standard deviation of the decision scores respectively. The threshold
is set that any value beyond an absolute z-score of 1 is considered
and outlier.
"""
def __init__(self, random_state=1234):
self.random_state = random_state
[docs]
def eval(self, decision):
"""Outlier/inlier evaluation process for decision scores.
Parameters
----------
decision : np.array or list of shape (n_samples)
or np.array of shape (n_samples, n_detectors)
which are the decision scores from a
outlier detection.
Returns
-------
outlier_labels : numpy array of shape (n_samples,)
For each observation, tells whether or not
it should be considered as an outlier according to the
fitted model. 0 stands for inliers and 1 for outliers.
"""
decision = check_scores(decision, random_state=self.random_state)
decision = normalize(decision)
self.dscores_ = decision
# Get the zscore of the decision scores
zscore = np.abs(stats.zscore(decision))
# Set the limit to where the zscore is 1
scores = np.zeros(len(decision), dtype=int)
mask = np.where(zscore >= 1.0)
scores[mask] = 1
self.thresh_ = None
return scores