Detector Base Classes
- class darts.ad.detectors.detectors.Detector(*args, **kwargs)[source]¶
Bases:
ABC
Base class for all detectors
Methods
detect
(series)Detect anomalies on given time series.
eval_accuracy
(actual_anomalies, anomaly_score)Score the results against true anomalies.
- detect(series)[source]¶
Detect anomalies on given time series.
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – series on which to detect anomalies.- Returns
binary prediciton (1 if considered as an anomaly, 0 if not)
- Return type
Union[TimeSeries, Sequence[TimeSeries]]
- eval_accuracy(actual_anomalies, anomaly_score, window=1, metric='recall')[source]¶
Score the results against true anomalies.
- Parameters
actual_anomalies (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – The ground truth of the anomalies (1 if it is an anomaly and 0 if not).anomaly_score (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – Series indicating how anomoulous each window of size w is.window (
int
) – Integer value indicating the number of past samples each point represents in the anomaly_score.metric (
str
) – Metric function to use. Must be one of “recall”, “precision”, “f1”, and “accuracy”. Default: “recall”
- Returns
Metric results for each anomaly score
- Return type
Union[float, Sequence[float], Sequence[Sequence[float]]]
- class darts.ad.detectors.detectors.FittableDetector(*args, **kwargs)[source]¶
Bases:
Detector
Base class of Detectors that need training.
Methods
detect
(series)Detect anomalies on given time series.
eval_accuracy
(actual_anomalies, anomaly_score)Score the results against true anomalies.
fit
(series)Trains the detector on the given time series.
fit_detect
(series)Trains the detector and detects anomalies on the same series.
- detect(series)[source]¶
Detect anomalies on given time series.
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – series on which to detect anomalies.- Returns
binary prediciton (1 if considered as an anomaly, 0 if not)
- Return type
Union[TimeSeries, Sequence[TimeSeries]]
- eval_accuracy(actual_anomalies, anomaly_score, window=1, metric='recall')¶
Score the results against true anomalies.
- Parameters
actual_anomalies (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – The ground truth of the anomalies (1 if it is an anomaly and 0 if not).anomaly_score (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – Series indicating how anomoulous each window of size w is.window (
int
) – Integer value indicating the number of past samples each point represents in the anomaly_score.metric (
str
) – Metric function to use. Must be one of “recall”, “precision”, “f1”, and “accuracy”. Default: “recall”
- Returns
Metric results for each anomaly score
- Return type
Union[float, Sequence[float], Sequence[Sequence[float]]]
- fit(series)[source]¶
Trains the detector on the given time series.
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – Time series to be used to train the detector.- Returns
Fitted Detector.
- Return type
self
- fit_detect(series)[source]¶
Trains the detector and detects anomalies on the same series.
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – Time series to be used for training and be detected for anomalies.- Returns
Binary prediciton (1 if considered as an anomaly, 0 if not)
- Return type
Union[TimeSeries, Sequence[TimeSeries]]