Multivariate Detector

class dtaianomaly.anomaly_detection.MultivariateDetector(detector: BaseDetector, aggregation: str = 'max', raise_warning_for_univariate: bool = True)[source]

Multivariate wrapper for anomaly detection.

Wraps around existing anomaly detectors to detect anomalies in multivariate time series. This is done by detecting anomalies in each attribute independently. This approach lifts univariate models to the multivariate setting. In addition, methods which detect anomalies using a multivariate sliding window (e.g., all PyODAnomalyDetector) receive a lot of input features. The MultivariateDetector limits the amount of input features, which may improve the performance.

Note that each feature is handled independently, which makes it impossible to detect anomalies based on the relation of multiple attributes.

Parameters:

  • detector (BaseDetector) – The anomaly detector used to detect anomalies in each attribute.

  • aggregation ({'min', 'max', 'mean'}, default='max') – Manner to aggregate the anomaly scores across each dimension.

  • raise_warning_for_univariate (bool, default=True) – Whether to raise a warning when the model is fitted on a univariate time series. Teh value does not change the output of the model, but only serves to surpress the warning message.

fitted_detectors_

The fitted anomaly detectors, one for each attribute.

Type:

list of BaseDetector

Examples

>>> import numpy as np
>>> from dtaianomaly.anomaly_detection import MultivariateDetector, IsolationForest
>>> x = np.array([[4, 8], [1, 2], [0, 1], [6, 5], [1, 4], [4, 3], [0, 9], [8, 2], [4, 5], [8, 3]])
>>> detector = MultivariateDetector(IsolationForest(window_size=3, random_state=0), aggregation='mean').fit(x)
>>> detector.decision_function(x)
array([-0.03045931, -0.04993609, -0.05237944, -0.07038518, -0.05778077,
       -0.0489984 , -0.02691477, -0.02928812, -0.02847268, -0.0387197 ])
check_is_fitted() → None

Check whether this anomaly detector is fitted or not.

Raises:

NotFittedError – If this detector is not fitted yet.

decision_function(X: ndarray) → array

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, **kwargs) → BaseDetector

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

is_fitted() → bool

Return whether this anomaly detector is fitted.

Returns:

is_fitted – True if and only if this detector is fitted, and can be used for detecting anomalies.

Return type:

bool

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.