import inspect
import numpy as np
from .base import BaseThresholder
from .thresh_utility import check_scores, cut, normalize
[docs]
class IQR(BaseThresholder):
r"""IQR class for Inter-Qaurtile Region thresholder.
Use the inter-quartile region to evaluate a non-parametric
means to threshold scores generated by the decision_scores
where outliers are set to any value beyond the third quartile
plus 1.5 times the inter-quartile region.
See :cite:`bardet2015iqr` 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 inter-quartile region is given as:
.. math::
IQR = \lvert Q_3-Q_1 \rvert
where :math:`Q_1` and :math:`Q_3` are the first and third quartile
respectively. The threshold for the decision scores is set as:
.. math::
t = Q_3 + 1.5 IQR
"""
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
arg_map = {'old': 'interpolation', 'new': 'method'}
arg_name = (arg_map['new'] if 'method' in
inspect.signature(np.percentile).parameters
else arg_map['old'])
# First quartile (Q1)
P1 = np.percentile(decision, 25, **{arg_name: 'midpoint'})
# Third quartile (Q3)
P3 = np.percentile(decision, 75, **{arg_name: 'midpoint'})
# Calculate IQR and generate limit
iqr = abs(P3-P1)
limit = P3 + 1.5*iqr
self.thresh_ = limit
return cut(decision, limit)