KMeans Anomaly Detector

class dtaianomaly.anomaly_detection.KMeansAnomalyDetector(window_size: int | str, stride: int = 1, **kwargs)[source]

Use KMeans clustering to detect anomalies.

KMeans anomaly detector [yairi2001fault] first clusters the data using the KMeasn clustering algorithm. Next, for new data, the corresponding cluster is predicted, and the distance to the cluster centroid is computed. This distance corresponds to the decision scores of this anomaly detector: if an instance is far from the centroid, it is more anomalous. The input of KMeans clustering is a sliding window.

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 KMeans clustering of scikit-learn anomaly detector

window_size_

The effectively used window size for this anomaly detector

Type:

int

k_means_

The KMeans clustering algorithm from scikit-learn

Type:

KMeans

Examples

>>> from dtaianomaly.anomaly_detection import KMeansAnomalyDetector
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> kmeans_ad = KMeansAnomalyDetector(10).fit(x)
>>> kmeans_ad.decision_function(x)
array([0.50321076, 0.5753145 , 0.61938076, ..., 0.29794485, 0.30720306,
       0.29857479])

References

[yairi2001fault]

T. Yairi, Y. Kato, and K. Hori. Fault detection by mining association rules from house-keeping data. In proceedings of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space, volume 18, page 21. Citeseer, 2001.

decision_function(X: ndarray) → ndarray[source]

Abstract method, compute anomaly scores.

Parameters:

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

Returns:

decision_scores – The computed anomaly scores.

Return type:

array-like of shape (n_samples)

fit(X: ndarray, y: ndarray | None = None, **kwargs) → KMeansAnomalyDetector[source]

Abstract method, fit this detector to the given data.

Parameters:

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

  • y (array-like, default=None) – Ground-truth information.

Returns:

self – Returns the instance itself.

Return type:

BaseDetector

predict_confidence(X: ndarray, X_train: ndarray = None, contamination: float = 0.05, decision_scores_given: bool = False)

Predict the confidence of the anomaly scores on the test given test data.

This method implements ExCeeD [perini2020quantifying] (Example-wise Confidence of anomaly Detectors) to estimate the confidence. ExCeed transforms the predicted decision scores to probability estimates using a Bayesian approach, which enables to assign a confidence score to each prediction which captures the uncertainty of the anomaly detector in that prediction.

Parameters:

  • X (array-like of shape (n_samples, n_attributes)) – The test time series for which the confidence of anomaly scores should be predicted.

  • X_train (array-like of shape (n_samples_train, n_attributes), default=None) – The training time series, which can be used as reference. If X_train=None, the test set is used as reference set.

  • contamination (float, default=0.05) – The (estimated) contamination rate for the data, i.e., the expected percentage of anomalies.

  • decision_scores_given (bool, default=False) – Whether the given X and X_train represent time series data or decision scores. If decision_scores_given=False (default), then the given arrays are interpreted as time series. Otherwise, they are interpreted as decision scores, as computed by decision_function().

Returns:

confidence – The confidence of this anomaly detector in each prediction in the given test time series.

Return type:

array-like of shape (n_samples)

References

[perini2020quantifying]

Perini, L., Vercruyssen, V., Davis, J. Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions. In: Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Springer, Cham, doi: 10.1007/978-3-030-67664-3_14.

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.