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