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Constant thresholder.

Each anomaly score is thresholded into a 0 or a 1.


  • anomaly_detector (river.anomaly.base.AnomalyDetector)

    The anomaly detector that will learn and produce scores.

  • threshold (float)

    The threshold to apply.


>>> from river import anomaly
>>> from river import compose
>>> from river import datasets
>>> from river import metrics
>>> from river import preprocessing

>>> model = compose.Pipeline(
...     preprocessing.MinMaxScaler(),
...     anomaly.ConstantThresholder(
...         anomaly.HalfSpaceTrees(seed=42),
...         threshold=0.8
...     )
... )

>>> report = metrics.ClassificationReport()

>>> for x, y in datasets.CreditCard().take(8000):
...     score = model.score_one(x)
...     model = model.learn_one(x)
...     report = report.update(y, score)

>>> report
           Precision   Recall   F1      Support
       0       0.999    0.939   0.968      7975
       1       0.038    0.760   0.072        25
   Macro       0.518    0.850   0.520
   Micro       0.939    0.939   0.939
Weighted       0.996    0.939   0.965
                 93.9% accuracy



Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.


Update the model.


  • x (dict)


AnomalyDetector: self


Return an outlier score.

A high score is indicative of an anomaly. A low score corresponds a normal observation.


  • x (dict)


float: An anomaly score. A high score is indicative of an anomaly. A low score corresponds a