import numpy as np
import scipy
from pyod.models.lof import LOF
from dtaianomaly.anomaly_detection._BaseDetector import Supervision
from dtaianomaly.anomaly_detection._BasePyODAnomalyDetector import (
BasePyODAnomalyDetector,
)
from dtaianomaly.type_validation import IntegerAttribute
from dtaianomaly.windowing import WINDOW_SIZE_TYPE
__all__ = ["LocalOutlierFactor"]
[docs]
class LocalOutlierFactor(BasePyODAnomalyDetector):
"""
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.
n_neighbors : int, default=20
The number of neighbors to use for the nearest neighbor queries.
metric : str, default='minkowski'
Distance metric for distance computations. any metric of scikit-learn and
scipy.spatial.distance can be used.
**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]...)
"""
n_neighbors: int
metric: str
attribute_validation = {"n_neighbors": IntegerAttribute(1)}
def __init__(
self,
window_size: WINDOW_SIZE_TYPE,
stride: int = 1,
n_neighbors: int = 20,
metric: str = "minkowski",
**kwargs,
):
scipy.spatial.distance.pdist(np.array([[0, 0], [1, 1]]), metric=metric)
self.n_neighbors = n_neighbors
self.metric = metric
super().__init__(window_size, stride, **kwargs)
def _initialize_detector(self, **kwargs) -> LOF:
return LOF(n_neighbors=self.n_neighbors, metric=self.metric, **kwargs)
def _supervision(self):
return Supervision.UNSUPERVISED