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# Authors: Denis Engemann <denis.engemann@gmail.com>
# License: BSD-3-Clause
import numpy as np
def _find_outliers(X, threshold=3.0, max_iter=2, tail=0):
"""Find outliers based on iterated Z-scoring.
This procedure compares the absolute z-score against the threshold.
After excluding local outliers, the comparison is repeated until no
local outlier is present any more.
Parameters
----------
X : np.ndarray of float, shape (n_elemenets,)
The scores for which to find outliers.
threshold : float
The value above which a feature is classified as outlier.
max_iter : int
The maximum number of iterations.
tail : {0, 1, -1}
Whether to search for outliers on both extremes of the z-scores (0),
or on just the positive (1) or negative (-1) side.
Returns
-------
bad_idx : np.ndarray of int, shape (n_features)
The outlier indices.
"""
from scipy.stats import zscore
my_mask = np.zeros(len(X), dtype=bool)
for _ in range(max_iter):
X = np.ma.masked_array(X, my_mask)
if tail == 0:
this_z = np.abs(zscore(X))
elif tail == 1:
this_z = zscore(X)
elif tail == -1:
this_z = -zscore(X)
else:
raise ValueError("Tail parameter %s not recognised." % tail)
local_bad = this_z > threshold
my_mask = np.max([my_mask, local_bad], 0)
if not np.any(local_bad):
break
bad_idx = np.where(my_mask)[0]
return bad_idx
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