Local Outlier Factor

class dtaianomaly.anomaly_detection.LocalOutlierFactor(window_size: str | int, stride: int = 1, **kwargs)

Anomaly detector based on the Local Outlier Factor.

The local outlier factor [Breunig2000LOF] 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.

Notes

The Local Outlier Factor inherets from 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 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

window_size_

The effectively used window size for this anomaly detector

Type:

int

pyod_detector_

A LOF-detector of PyOD

Type:

LOF

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)
array([0.98370943, 0.98533454, 0.98738196, ..., 1.02394282, 1.02648068,
       1.01827158])

References

[Breunig2000LOF]

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander. 2000. LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (SIGMOD ‘00). Association for Computing Machinery, New York, NY, USA, 93–104. doi: 10.1145/342009.335388

decision_function(X: ndarray) → ndarray

Compute decision scores.

Parameters:

X (array-like of shape (n_samples, n_attributes)) – Input time series.

Returns:

decision_scores – The decision scores of the anomaly detector. Higher indicates more anomalous.

Return type:

array-like of shape (n_samples)

Raises:

• ValueError – If X is not a valid array.

• NotFittedError – If this method is called before fitting the anomaly detector.

fit(X: ndarray, y: ndarray | None = None, **kwargs) → BaseDetector

Fit this PyOD anomaly detector on the given data.

Parameters:

• X (array-like of shape (n_samples, n_attributes)) – Input time series.

• y (ignored) – Not used, present for API consistency by convention.

• kwargs – Additional parameters to be passed to compute_window_size().

Returns:

self – Returns the instance itself

Return type:

PyODAnomalyDetector

Raises:

ValueError – If X is not a valid array.

predict_proba(X: ndarray) → ndarray

Predict anomaly probabilities

Estimate the probability of a sample of X being anomalous, based on the anomaly scores obtained from decision_function by rescaling them to the range of [0, 1] via min-max scaling.

Parameters:

X (array-like of shape (n_samples, n_attributes)) – Input time series.

Returns:

anomaly_scores – 1D array with the same length as X, with values in the interval [0, 1], in which a higher value implies that the instance is more likely to be anomalous.

Return type:

array-like of shape (n_samples)

Raises:

• ValueError – If scores is not a valid array.

• ValueError – If the prediction scores from ‘decision_function’ are constant, but not in the interval [0, 1], because these values can not unambiguously be transformed to an anomaly probability.

save(path: str | Path) → None

Save detector to disk as a pickle file with extension .dtai. If the given path consists of multiple subdirectories, then the not existing subdirectories are created.

Parameters:

path (str or Path) – Location where to store the detector.