from pyod.models.hbos import HBOS
from dtaianomaly.anomaly_detection.BaseDetector import Supervision
from dtaianomaly.anomaly_detection.PyODAnomalyDetector import PyODAnomalyDetector
[docs]
class HistogramBasedOutlierScore(PyODAnomalyDetector):
"""
Anomaly detector based on the Histogram Based Outlier Score (HBOS) algorithm.
Histogram Based Outlier Score (HBOS) [goldstein2012histogram]_ constructs for each feature
a univariate histogram. Bins with a small height (for static bin widths) or wider bins (for
dynamic bin widths) correspond to sparse regions of the feature space. Thus, values falling
in these bins lay in sparse regions of the feature space and are considered more anomalous.
In this implementation, it is possible to set a window size to take the past observations into
account. However, HBOS assumes feature independence. Therefore, for a time series with :math:`D`
attributes and a window size :math:`w`, HBOS constructs :math:`D \\times w` independent histograms,
from which the anomaly score is computed.
Notes
-----
The HBOS detector inherets from :py:class:`~dtaianomaly.anomaly_detection.PyODAnomalyDetector`.
Parameters
----------
window_size: int or str
The window size to use for extracting sliding windows from the time series. This
value will be passed to :py:meth:`~dtaianomaly.anomaly_detection.compute_window_size`.
stride: int, default=1
The stride, i.e., the step size for extracting sliding windows from the time series.
**kwargs
Arguments to be passed to the PyOD histogram based outlier score.
Attributes
----------
window_size_: int
The effectively used window size for this anomaly detector
pyod_detector_ : HBOS
An HBOS detector of PyOD
Examples
--------
>>> from dtaianomaly.anomaly_detection import HistogramBasedOutlierScore
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> hbos = HistogramBasedOutlierScore(1).fit(x)
>>> hbos.decision_function(x)
array([0.51808795, 0.51808795, 0.51808795, ..., 0.48347552, 0.48347552,
0.48347552])
References
----------
.. [goldstein2012histogram] Goldstein, Markus, and Andreas Dengel. "Histogram-based outlier score (hbos):
A fast unsupervised anomaly detection algorithm." KI-2012: poster and demo track 1 (2012): 59-63.
"""
def _initialize_detector(self, **kwargs) -> HBOS:
return HBOS(**kwargs)
def _supervision(self):
return Supervision.UNSUPERVISED