from pyod.models.lof import LOF
from dtaianomaly.anomaly_detection.BaseDetector import Supervision
from dtaianomaly.anomaly_detection.PyODAnomalyDetector import PyODAnomalyDetector
[docs]
class LocalOutlierFactor(PyODAnomalyDetector):
"""
Anomaly detector based on the Local Outlier Factor :cite:`breunig2000lof`.
The local outlier factor compares the density of each
sample to the density of the neighboring samples. If the neighbors of a
sample have a much higher density that the sample itself, the sample is
considered anomalous. By looking at the local density (i.e., only comparing
with the neighbors of a sample), the local outlier factor takes into
account varying densities across the sample space.
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 local outlier factor
Attributes
----------
window_size_: int
The effectively used window size for this anomaly detector
pyod_detector_ : LOF
A LOF-detector of PyOD
Examples
--------
>>> from dtaianomaly.anomaly_detection import LocalOutlierFactor
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> local_outlier_factor = LocalOutlierFactor(10).fit(x)
>>> local_outlier_factor.decision_function(x) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
array([0.98370943, 0.98533454, 0.98738196, ..., 1.02394282, 1.02648068, 1.01827158]...)
Notes
-----
The Local Outlier Factor inherets from :py:class:`~dtaianomaly.anomaly_detection.PyodAnomalyDetector`.
"""
def _initialize_detector(self, **kwargs) -> LOF:
return LOF(**kwargs)
def _supervision(self):
return Supervision.UNSUPERVISED